CN117934732A

CN117934732A - Reconstruction method and device of point cloud model and electronic equipment

Info

Publication number: CN117934732A
Application number: CN202410329931.XA
Authority: CN
Inventors: 花如祥
Original assignee: Beijing CHL Robotics Co ltd
Current assignee: Beijing CHL Robotics Co ltd
Priority date: 2024-03-22
Filing date: 2024-03-22
Publication date: 2024-04-26
Anticipated expiration: 2044-03-22
Also published as: CN117934732B

Abstract

The application provides a reconstruction method and device of a point cloud model and electronic equipment, and relates to a three-dimensional reconstruction neighborhood, wherein the method comprises the following steps: acquiring a reference point cloud model of a target object, wherein the reference point cloud model comprises a plurality of reference shapes, and each reference shape in the plurality of reference shapes corresponds to one reference data point respectively; determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively; connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches; and performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the target patches are in one-to-one correspondence with the plurality of patch shapes, and the plurality of patch shapes are patches with a plurality of preset shapes, so that the accuracy of reconstructing the point cloud model is improved.

Description

Reconstruction method and device of point cloud model and electronic equipment

Technical Field

The application relates to the field of three-dimensional reconstruction, in particular to a reconstruction method and device of a point cloud model and electronic equipment.

Background

In the process of spraying an object to be sprayed by a robot, in order to improve the spraying efficiency and accurately spray the sprayable position of the object to be sprayed, a spraying path of the robot needs to be reasonably planned, while the existing path planning method is only suitable for planning a monochromatic full-coverage spraying path of the object to be sprayed, but the mode cannot meet the special and multicolor requirements of the shape of a digital camouflage (the design of a camouflage pattern based on a digital technology), so that a point cloud model of the object to be sprayed needs to be reconstructed before the object to be sprayed is sprayed, the specific sprayable position of the object to be sprayed is determined, and a model foundation is laid for planning a multicolor digital camouflage path subsequently.

In the related art, the mode of reconstructing the model is to map a three-dimensional point cloud model into a plane, and reconstruct the point cloud model according to the data point information in the point cloud mapped into the plane, but the model reconstruction mode is only suitable for reconstructing a model with a small number of points in the point cloud, so that no large deviation exists on the positions of the data points in the three-dimensional space, but for the point cloud model formed by a large number of data points, the mapping of the large number of data points not only causes the increase of the calculated amount, but also causes the superposition of the mapped data points in the plane, so that the problem of low accuracy exists in the existing reconstruction method of the point cloud model is seen.

Aiming at the problem of low accuracy of the existing point cloud model reconstruction method, no effective technical solution is proposed at present.

Disclosure of Invention

The embodiment of the application provides a method for reconstructing a point cloud model, which at least solves the problem of low accuracy of the existing point cloud model reconstruction method.

According to an aspect of the embodiment of the present application, there is provided a method for reconstructing a point cloud model, including: acquiring a reference point cloud model of a target object, wherein the reference point cloud model comprises a plurality of reference shapes, the reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one reference data point; determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are obtained by connecting the plurality of reference data points in different connection modes according to a first connection rule, and each of the plurality of reference grids corresponds to a plurality of reference grid vertexes; connecting the plurality of reference grid vertices according to a second connection rule to obtain a reference patch set, wherein the reference patch set comprises a plurality of reference patches; and performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches are in one-to-one correspondence with a plurality of patch shapes, and the plurality of patch shapes are patches with a plurality of preset shapes.

According to another aspect of the embodiment of the present application, there is also provided a reconstruction device of a point cloud model, including: an obtaining unit, configured to obtain a reference point cloud model of a target object, where the reference point cloud model includes a plurality of reference shapes, the plurality of reference shapes corresponding to a plurality of reference data points, and each of the reference shapes corresponds to one of the reference data points; a determining unit, configured to determine a grid model corresponding to the reference point cloud model according to the reference point cloud model, where the grid model includes a plurality of reference grids, where the plurality of reference grids are a plurality of grids obtained by connecting the plurality of reference data points in different connection manners according to a first connection rule, and each of the plurality of reference grids corresponds to a plurality of reference grid vertices; the connection unit is used for connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches; and the processing unit is used for carrying out target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches are in one-to-one correspondence with the plurality of patch shapes, and the plurality of patch shapes are patches with a plurality of preset shapes.

Optionally, the determining unit includes a first acquiring unit and a first connecting unit, where the first acquiring unit is configured to acquire an adjacent relationship between the plurality of reference shapes in the reference point cloud model; the first connection unit is configured to connect reference data points corresponding to the plurality of reference shapes in the reference point cloud model according to an adjacent relationship between the plurality of reference shapes, so as to obtain a grid model corresponding to the reference point cloud model, where the adjacent relationship includes: any one or a combination of a plurality of surface adjacency, edge adjacency and point adjacency.

Optionally, the first connection unit includes a second obtaining unit, a selecting unit, and a first connection subunit, where the second obtaining unit is configured to obtain a plurality of first reference shapes corresponding to the plurality of reference shapes, where each reference shape has a plurality of adjacent relationships with the plurality of first reference shapes, and each reference shape has a single adjacent relationship with each first reference shape; the selecting unit is configured to select a plurality of second reference shapes that satisfy a preset condition from the plurality of first reference shapes according to preset priorities of the plurality of adjacent relationships; and the first connection subunit is configured to connect the reference data points of the plurality of reference shapes with the reference data points corresponding to the plurality of second reference shapes, so as to obtain a grid model corresponding to the reference point cloud model.

Optionally, the connection unit includes a third obtaining unit, a first determining unit, and a second connection unit, where the third obtaining unit is configured to obtain, from the plurality of reference grid vertices, a plurality of first reference grid vertices corresponding to the plurality of reference grid vertices, respectively; the first determining unit is configured to determine a plurality of second reference mesh vertices that satisfy the second connection rule from the plurality of first reference mesh vertices; the second connection unit is configured to connect the plurality of reference grid vertices with the plurality of second reference grid vertices according to a second connection rule, so as to obtain a reference patch set.

Optionally, the processing unit includes a second determining unit, a third determining unit, a fourth determining unit, a fifth determining unit, a first splitting unit, and a sixth determining unit, where the second determining unit is configured to determine, according to a plurality of the reference patches, patch shapes corresponding to the plurality of the reference patches respectively; the third determining unit is configured to determine a first panel set and a second panel set according to the determined plurality of panel shapes, where the first panel set includes a plurality of first panels that satisfy a preset splitting condition, and the second panel set includes a plurality of second panels other than the plurality of first panels; the fourth determining unit is configured to determine reference planes corresponding to the plurality of first panels, respectively, by using a least square method on the plurality of first panels; the fifth determining unit is configured to determine fitting errors of each of a plurality of diagonals corresponding to the reference plane according to a plurality of vertex coordinates of the reference plane, so as to obtain a plurality of fitting errors; the first splitting unit is used for splitting the reference plane according to the fitting errors to obtain a plurality of third panels; the sixth determining unit is configured to determine the target point cloud model according to the plurality of third panels and the plurality of second panels.

Optionally, the reconstructing device of the point cloud model further includes: a screening unit, a seventh determining unit, an eighth determining unit, and a ninth determining unit, where the screening unit is configured to perform a patch screening operation on the plurality of second patches and the plurality of third patches after performing a splitting operation on the reference plane according to a plurality of fitting errors, so as to obtain a plurality of fourth patches, where shapes of patches corresponding to the plurality of fourth patches are the same; the seventh determining unit is configured to determine a plurality of angle errors corresponding to the fourth patches, where each fourth patch corresponds to one of the angle errors, and the angle errors are used to indicate a sum of errors of a plurality of internal angles of the fourth patch and a preset angle; the eighth determining unit is configured to determine a plurality of edge length errors corresponding to the fourth patches, where each fourth patch corresponds to one of the edge length errors, and the edge length error is used to indicate a sum of errors of a plurality of edge lengths of the fourth patches and a preset length; the ninth determination unit is configured to determine a plurality of target patches from the plurality of fourth patches based on the plurality of angle errors and the plurality of edge errors.

Optionally, the reconstructing device of the point cloud model further includes: the device comprises a fourth acquisition unit, a tenth determination unit, a second splitting unit, a deletion unit and a determination subunit, wherein the fourth acquisition unit is used for acquiring point cloud data of the target object before acquiring a reference point cloud model of the target object; the tenth determining unit is configured to determine a bounding box corresponding to the point cloud data according to the position information of each of the plurality of first data points in the point cloud data, where the bounding box is used to indicate an optimal bounding space of the point cloud data; the second splitting unit is used for splitting the bounding box according to a preset size to obtain a plurality of splitting shapes; the deleting unit is configured to delete a split shape in which no second data point exists in the plurality of split shapes, to obtain the plurality of reference shapes, where the second data point is any one of the plurality of first data points; the determining subunit is configured to determine, according to first data points included in each of the plurality of reference shapes, the plurality of reference data points corresponding to the plurality of reference shapes, and obtain a reference point cloud model including the plurality of reference shapes.

According to yet another aspect of an embodiment of the present application, there is provided a computer-readable storage medium storing computer instructions for causing a computer to perform a reconstruction method of a point cloud model as above.

According to still another aspect of the embodiment of the present application, there is also provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform a reconstruction method of a point cloud model as above.

Compared with the prior art, the technical scheme provided by the embodiment of the application can have the following beneficial effects:

Determining a corresponding grid model according to the reference point cloud model by acquiring the reference point cloud model of the target object, and connecting a plurality of reference grid vertexes of the grid model according to a second connection rule to obtain a patch set; and performing target processing on the plurality of reference patches to obtain a final point cloud model. The method can realize the actual reconstruction process of the point cloud model, is convenient for removing the irregular patches from the original model, leaves the regular patches which need to be reserved, avoids the problem of lower accuracy of the point cloud model reconstruction method caused by the fact that data points in a plurality of three-dimensional spaces are mapped into a two-dimensional plane and the data points are overlapped, and accordingly improves the accuracy of the point cloud model reconstruction method.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a hardware environment of an alternative point cloud model reconstruction method according to an embodiment of the present invention;

FIG. 2 is a flow chart of an alternative method of reconstructing a point cloud model according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an alternative method of reconstructing a point cloud model according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of another alternative method of reconstructing a point cloud model according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of yet another alternative method of reconstructing a point cloud model according to an embodiment of the present invention;

FIG. 6 is a flow chart of another alternative method of reconstructing a point cloud model according to an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of an alternative reconstruction device for a point cloud model according to an embodiment of the present invention;

Fig. 8 is a schematic structural view of an alternative electronic device according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that such data may be interchanged where appropriate such that embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.

The existing path planning method is suitable for planning a single-color full-coverage spraying path of an object to be sprayed, and because digital camouflage (digital camouflage is a camouflage pattern design based on a digital technology), the existing algorithm is not suitable for carrying out path planning on a point cloud model any more and can not directly carry out path planning on the point cloud model, and model reconstruction is needed aiming at the shape characteristics of the digital camouflage, so that a user can realize effective concealment and camouflage under various environments.

In order to solve the above-mentioned problems, the embodiment of the present application provides a method for reconstructing a point cloud model quickly into a patch model that can be used for spraying, and as an optional implementation manner, the method for reconstructing a point cloud model may be but not limited to be applied to a system for reconstructing a point cloud model formed by a terminal device 102 and a server 104 as shown in fig. 1. As shown in fig. 1, the terminal device 102 is connected to the server 104 through a network 110, where the network 110 may include, but is not limited to: a wired network, a wireless network, wherein the wired network comprises: local area networks, metropolitan area networks, and wide area networks, the wireless network comprising: bluetooth, WIFI, and other networks that enable wireless communications. The terminal device 102 may include, but is not limited to, at least one of: a Mobile phone (such as an Android Mobile phone, an iOS Mobile phone, etc.), a notebook computer, a tablet computer, a palm computer, an MID (Mobile INTERNET DEVICES, mobile internet device), a PAD, a desktop computer, a smart television, a vehicle-mounted device, etc.

The terminal device 102 is further provided with a display 106, a processor 108 and a memory 112, where the display 106 may be configured to display a reference point cloud model and a target point cloud model corresponding to the target object, the processor 108 may be configured to perform data processing on the collected point cloud data of the target object, and the memory 112 may be configured to store the point cloud data and the reference point cloud model and the target point cloud model. It may be understood that, in the case where the terminal device 102 receives the request for reconstructing the point cloud model of the user, the terminal device 102 may acquire the point cloud data of the target object by means of scanning, and the like, and send the acquired point cloud data to the server 104 through the network 110, and the server 104 may generate the reference point cloud model of the target object through the acquired point cloud data, so as to perform a specific model reconstruction process on the generated reference point cloud model.

The server 104 may be a single server, a server cluster composed of a plurality of servers, or a cloud server. The server 104 includes a database 114 and a processing engine 116. The database 114 may be configured to store point cloud data, a reference point cloud model, and a target point cloud model, and the processing engine 116 may be configured to process the point cloud data.

According to an aspect of the embodiment of the present invention, the above-mentioned reconstruction system of a point cloud model may further perform the following steps: first, the terminal device 102 executes S102 to S104, acquires point cloud data of a target object, and sends the point cloud data to the server 104 through the network 110; next, the server 104 executes S106 to S114: generating a reference point cloud model of the target object according to the point cloud data; acquiring a reference point cloud model of a target object, wherein the reference point cloud model comprises a plurality of reference shapes, the plurality of reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one reference data point respectively; determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are a plurality of grids obtained by connecting a plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively; connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches; performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches correspond to the plurality of patch shapes one by one, and the plurality of patch shapes are patches with a plurality of preset shapes; the server 104 then performs S116: the target point cloud model is sent to the terminal device 102.

In the above embodiment of the present invention, a reference point cloud model for acquiring a target object is adopted, where the reference point cloud model includes a plurality of reference shapes, the plurality of reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to a reference data point; determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are a plurality of grids obtained by connecting a plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively; the whole reference point cloud model is split into a grid model comprising a plurality of reference grids, so that smaller reference grids which do not meet the condition can be removed conveniently in the model reconstruction process; connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches; the multiple reference grids are skillfully converted into the reference patches, and the reference patches are not overlapped; the accuracy of the model in the reconstruction process of the point cloud model is ensured; performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches correspond to the plurality of patch shapes one by one, and the plurality of patch shapes are patches with a plurality of preset shapes; determining a dough sheet satisfying a preset shape from a plurality of dough sheets; the patches which do not meet the conditions are removed, so that the accuracy of path planning when the target point cloud model obtained after reconstruction is sprayed is improved, the accuracy of a reconstruction method of the point cloud model is improved, and the technical effect of high-accuracy reconstruction of the point cloud model is achieved.

The above is merely an example, and there is no limitation in this embodiment.

As an optional implementation manner, as shown in fig. 2, the above-mentioned method for reconstructing a point cloud model may include the following steps:

S202, acquiring a reference point cloud model of a target object, wherein the reference point cloud model comprises a plurality of reference shapes, the plurality of reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one reference data point respectively;

s204, determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are a plurality of grids obtained by connecting a plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively;

S206, connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference patch set, wherein the reference patch set comprises a plurality of reference patches;

S208, performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches correspond to the plurality of patch shapes one by one, and the plurality of patch shapes are patches with a plurality of preset shapes.

It should be noted that, the target object in S202 may be, but not limited to, an object to be sprayed, and the target object may include a person or an object, such as a vehicle, clothes, a soldier, and the like. The reference point cloud model may be, but is not limited to, a multi-dimensional point cloud model corresponding to the target object, which is generated according to a plurality of data points in a reference point cloud corresponding to the target object, where the multi-dimensions include two dimensions, three dimensions, four dimensions, and the like, and the reference point cloud may be obtained by, but is not limited to, scanning the target object. The plurality of reference shapes may be the same or different, and take the reference point cloud model as the three-dimensional point cloud model as an example, the plurality of reference shapes may be the shape of a three-dimensional structure, and the plurality of reference shapes may be cubes, cuboids, cylinders, cones, and the like. A plurality of data points (data points in a reference point cloud corresponding to the target object) may be included within each of the plurality of reference shapes, and the reference data point corresponding to each reference shape may be, but is not limited to, a unique one more representative point determined from the plurality of data points within each reference shape. It is to be understood that for convenience of description, reference data points will be referred to as simplified points in the following embodiments.

The mesh model in S204 may be, but is not limited to, understood as a connection rule obtained by connecting two data points in the reference data points corresponding to each reference shape according to a first connection rule, where the first connection rule may be, but is not limited to, understood as a connection rule related to an adjacent relationship between a plurality of reference shapes, for example, each reference shape has a plurality of other reference shapes having an adjacent relationship with the reference shape, but an adjacent relationship between the other reference shapes is different, and the adjacent relationship, for example, a surface adjacent relationship, an edge adjacent relationship, a point adjacent relationship, connects the plurality of reference data points according to a priority of the different adjacent relationship. The different connection modes can be understood as follows, but are not limited to: the grid shapes corresponding to the grids obtained by connection according to the same connection rule (namely, the first connection rule) and the grid vertices (namely, the reference grid vertices) included in the grid shapes are different, for example, when the grid shapes corresponding to the grids obtained by the different connection modes are rectangular or square, the number of the reference grid vertices corresponding to the shapes is 4; for example, when the mesh shape corresponding to the mesh obtained by the different connection method is a triangle, the number of reference mesh vertices corresponding to the shape is 3, and when the reference shape corresponding to the mesh obtained by the different connection method is a pentagon, the number of reference mesh vertices corresponding to the shape is 5. Meanwhile, it is understood that the grid shapes corresponding to the plurality of reference grids may be regular shapes or irregular shapes (e.g., regular rectangles, trapezoids, etc.).

The target processing operation in S208 may be, but is not limited to, deleting a patch of the plurality of reference patches that does not satisfy a preset requirement, where the preset requirement includes a requirement on a patch shape corresponding to the reference patch, a requirement on whether the patch shape of the reference patch is regular, and the like.

According to the embodiment of the application, a reference point cloud model of a target object is acquired, wherein the reference point cloud model comprises a plurality of reference shapes, the plurality of reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one reference data point respectively; determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are a plurality of grids obtained by connecting a plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively; the whole reference point cloud model is split into a grid model comprising a plurality of reference grids, so that smaller reference grids which do not meet the condition can be removed conveniently in the model reconstruction process; connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches; the multiple reference grids are skillfully converted into the reference patches, and the reference patches are not overlapped; the accuracy of the model in the reconstruction process of the point cloud model is ensured; performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches correspond to the plurality of patch shapes one by one, and the plurality of patch shapes are patches with a plurality of preset shapes; determining a dough sheet satisfying a preset shape from a plurality of dough sheets; the patches which do not meet the conditions are removed, so that the accuracy of path planning when the target point cloud model obtained after reconstruction is sprayed is improved, the accuracy of a reconstruction method of the point cloud model is improved, and the technical effect of high-accuracy reconstruction of the point cloud model is achieved.

As an optional implementation manner, the determining the network model corresponding to the reference point cloud model according to the reference point cloud model includes:

s1, acquiring adjacent relations among a plurality of reference shapes in a reference point cloud model;

s2, connecting the reference data points corresponding to the reference shapes according to the adjacent relation among the reference shapes in the reference point cloud model to obtain a grid model corresponding to the reference point cloud model, wherein the adjacent relation comprises the following steps: any one or a combination of a plurality of surface adjacency, edge adjacency and point adjacency.

The adjacency relation (also called adjacency relation, connection relation) in S1 refers to a relation between elements connected by a common plane, a common edge, or a common vertex in some way. In graph theory, the relationships between all vertices can be described abstractly as a neighborhood relationship, which can then be formed into a graph. The above-mentioned adjacency relation includes: surface-adjacent, point-adjacent.

The operation in S2 may be, but is not limited to, understood as that the reference data points corresponding to each of the plurality of reference shapes are connected according to a plurality of adjacent relations between each of the plurality of reference shapes and other reference shapes, and when the reference data points corresponding to each of the plurality of reference shapes are specifically connected, the connection may be, but is not limited to, performed according to a preset priority of the plurality of adjacent relations.

With the above embodiment of the present application, the acquisition of the neighboring relationship between the plurality of reference shapes in the reference point cloud model is adopted; according to the adjacent relation among a plurality of reference shapes in the reference point cloud model, connecting the reference data points corresponding to the reference shapes respectively to obtain a grid model corresponding to the reference point cloud model, wherein the adjacent relation comprises the following steps: the method can reasonably convert the four-dimensional shape of a three-dimensional or a certain time frame into a two-dimensional shape in a mode of combining any one or more of surface adjacency, edge adjacency and point adjacency, thereby avoiding the problem of directly mapping a plurality of data points included in the four-dimensional shape of the three-dimensional or the certain time to the data point coverage on the two-dimensional plane, further solving the problem of low accuracy of the existing point cloud model reconstruction method and improving the accuracy of the point cloud model reconstruction method.

As an optional implementation manner, according to the adjacent relation between the plurality of reference shapes in the reference point cloud model, connecting the reference data points corresponding to the plurality of reference shapes respectively to obtain the grid model corresponding to the reference point cloud model includes:

S1, acquiring a plurality of first reference shapes corresponding to the plurality of reference shapes respectively, wherein each reference shape and the plurality of first reference shapes have a plurality of adjacent relations, and each reference shape and each first reference shape have an adjacent relation respectively;

s2, selecting a plurality of second reference shapes meeting preset conditions from a plurality of first reference shapes according to preset priorities of a plurality of adjacent relations;

And S3, connecting the reference data points of the plurality of reference shapes with the reference data points corresponding to the plurality of second reference shapes to obtain a grid model corresponding to the reference point cloud model.

It should be noted that the operation in S1 described above may be understood, but is not limited to, that a plurality of reference shapes having an adjacent relationship with each reference shape are determined from a plurality of reference shapes, for example, the plurality of reference shapes include a reference shape a, a reference shape B, a reference shape C, a reference shape D, a reference shape E, a reference shape B and a reference shape C having an adjacent relationship with the reference shape a, a reference shape a and a reference shape E having an adjacent relationship with the reference shape B, a reference shape a, a reference shape D, and a reference shape E having an adjacent relationship with the reference shape C, and B and a reference shape C having an adjacent relationship with the reference shape E.

The preset condition in S2 may be a neighbor relationship with the highest priority among the plurality of neighbor relationships.

The step S3 of connecting the reference data points of the plurality of reference shapes with the reference data points corresponding to the plurality of second reference shapes may be, but is not limited to, understood as connecting the reference data points corresponding to the plurality of reference shapes with the reference data points corresponding to the plurality of second reference shapes finally determined in step S2, and thus obtaining the network model corresponding to the reference point cloud model.

It will be appreciated that the above-described process of generating a mesh model comprising a plurality of reference meshes is a process of connecting adjacent cube simplified points according to a certain rule. For example, the plurality of reference shapes are cubes, and the adjacency between cubes can be divided into surface adjacency, edge adjacency, and point adjacency. Assume that the preset priority is: the surface adjacency > edge adjacency > point adjacency, the preset condition is the adjacency relationship with highest priority among a plurality of adjacency relationships, and then the specific connection process is as follows: if the square B with adjacent surfaces and the square C with adjacent edges are arranged around the square A, connecting the simplifying point of the square A with the simplifying point of the square B; if a cube B with adjacent surfaces and a cube D with adjacent points are arranged around the cube A, connecting the simplified points of the cube A with the simplified points of the cube B; if there are cubes C adjacent to each other around the cube a and cubes D adjacent to each other in point, the reduced points of the cube a are connected to the reduced points of the cube C.

The operations in S1 to S3 described above will be described below by taking fig. 3 as an example.

Fig. 3 (a) to 3 (c) illustrate connection rules of the simplified points (i.e., the above-described reference data points). Wherein, X shown in (a) of fig. 3 and (b) of fig. 3 represents a simplified point of the adjacent square of the surface; o shown in fig. 3 (a) and fig. 3 (c) represents a simplified point of the edge adjacent cube; fig. 3 (b) and fig. 3 (c) show simplified points of the adjacent cubes. The preset condition is that the priority of the adjacent relation is highest, assuming that the priority of the preset adjacent relation is that the face adjacent > side adjacent > point is adjacent.

The operations in S1 to S3 described above are, for example: as shown in fig. 3 (a), the determined reference shape 301 has a reference shape 302 and a reference shape 303 (i.e., the first reference shape) that have an adjacent relationship with the reference shape 301, the adjacent relationship between the reference shape 301 and the reference shape 302 is surface adjacent, the adjacent relationship between the reference shape 301 and the reference shape 303 is edge adjacent, the second reference shape meeting the preset condition selected from the reference shape 302 and the reference shape 303 is the reference shape 302, and then the reference data point corresponding to the reference shape 301 is connected with the reference data point corresponding to the reference shape 302; as shown in fig. 3 (b), the determined reference shape 304 has a reference shape 305 and a reference shape 306 (i.e. the first reference shape) that have an adjacent relationship, the adjacent relationship between the reference shape 304 and the reference shape 305 is surface adjacent, the adjacent relationship between the reference shape 304 and the reference shape 306 is point adjacent, the second reference shape meeting the preset condition selected from the reference shape 305 and the reference shape 306 is the reference shape 305, and then the reference data point corresponding to the reference shape 304 is connected with the reference data point corresponding to the reference shape 305; as shown in fig. 3 (c), the reference shape 307 has a reference shape 308 and a reference shape 309 (i.e., the first reference shape) that are determined to have an adjacent relationship with the reference shape 307, the adjacent relationship between the reference shape 307 and the reference shape 308 is point-adjacent, the adjacent relationship between the reference shape 307 and the reference shape 309 is edge-adjacent, the second reference shape selected from the reference shape 308 and the reference shape 309 and meeting the preset condition is the reference shape 309, and then the reference data point corresponding to the reference shape 307 is connected with the reference data point corresponding to the reference shape 309.

All grids obtained by the above connection rule can be composed of one set of two tuplesRepresentation, wherein, element/>And element/>Respectively, vertices and edges, as shown in equation one below:

Equation one

At this time, the grid set obtained by connectionThe middle part is mainly quadrilateral meshes, contains a small number of triangular meshes and pentagonal meshes, and a mesh area larger than the pentagon is defaulted to be a cavity area and is not calculated.

According to the embodiment of the application, a plurality of first reference shapes corresponding to the plurality of reference shapes are acquired, wherein each reference shape and the plurality of first reference shapes have a plurality of adjacent relations, and each reference shape and each first reference shape have an adjacent relation respectively; selecting a plurality of second reference shapes meeting preset conditions from the plurality of first reference shapes according to preset priorities of a plurality of adjacent relations; and connecting the reference data points of the plurality of reference shapes with the reference data points corresponding to the plurality of second reference shapes to obtain a grid model corresponding to the reference point cloud model, and skillfully converting the three-dimensional graph into the planar graph by adopting a reference data point mode, so that the calculated amount in the conversion process of the point cloud model is simplified, and the conversion efficiency of the point cloud model is improved.

As an optional implementation manner, the connecting the plurality of reference grid vertices according to the second connection rule to obtain the reference patch set includes:

s1, acquiring a plurality of first reference grid vertexes corresponding to a plurality of reference grid vertexes from a plurality of reference grid vertexes;

s2, determining a plurality of second reference grid vertexes meeting a second connection rule from the plurality of first reference grid vertexes;

and S3, respectively connecting the plurality of reference grid vertexes with the plurality of second reference grid vertexes according to a second connection rule to obtain a reference surface patch set.

It will be appreciated that a plurality of reference grids (i.e., the grid set described above) are obtained by concatenating the reference data pointsThe plurality of reference meshes included in the list) includes only edge structure and vertex information of the reference mesh (the reference mesh vertices), and does not include information of the plane structure, and therefore, it is necessary to perform the following steps/>The regular patch-shaped patches (for example, square patches and triangular patches) are obtained by processing the patch information. I.e. the operations in S1 to S3 described above may be understood, but are not limited to, to generate a dough sheet.

The operation of obtaining the plurality of first reference mesh vertices corresponding to the plurality of reference mesh vertices in S1 may be, but is not limited to, understood as follows: and determining the reference grid vertex adjacent to each reference grid vertex from the plurality of reference grid vertices to be the first reference grid vertex. The above-described adjacent reference mesh vertices may be understood, but are not limited to: vertices that may be directly connected to the reference mesh vertices and that do not pass through any other reference mesh vertices in between.

The second connection rule in S2 may be a plurality of reference grid vertices (i.e., the second reference grid vertices) that can form a shortest loop with the corresponding reference grid vertices.

The operation in S3 may be, but is not limited to, that the reference patch set including the plurality of reference patches is obtained by sequentially connecting the plurality of second reference mesh vertices determined in S2 and corresponding to each reference mesh vertex and satisfying the second connection rule.

The steps S1 to S3 may be based on the idea of shortest loop search, i.e. from the first of the plurality of reference gridsVertices/>Initially, along with it (vertex/>) Contiguous edges (/ >)) Find the next vertex/>The process is looped until the vertex/>Until and ensure a closed path/>The polygon formed by connecting the vertexes of the closed path is the generated patch. The algorithm comprises the following steps:

S1, selecting grid vertexes And from/>Departure edge (/ >)) Find vertices/>；

S2, judging the edge) If the number of times of use is more than 2, repeating the S1 to reselect the edge, otherwise continuing;

S3, from Starting from this, select and its (/ >) The connected edges are judged according to the rule in S2 to find the next vertex, and S2 and S3 are circulated until returning to/>And record the path length/>, of the loop at this timeOr stopping when the total edge number of the path is greater than 5;

S4, traversing the grid vertexes, and finding out the mesh vertexes to enable the mesh vertexes to be enabled The shortest loop;

and S5, selecting another vertex, and circulating the steps S1 to S4 until all grid vertices (namely a plurality of reference grid vertices corresponding to the plurality of reference grids) are traversed.

Assume that the patches generated by the polygonal mesh are，/>Is a set of tuples of vertices, then the patch can be represented by the following equation two:

Formula II

The resulting dough sheetMost of the panels are quadrilateral panels, including some triangular panels and a few pentagonal panels, which need to be further split and sorted to obtain a model including only regular quadrilateral panels and triangular panels.

According to the embodiment of the application, a plurality of first reference grid vertexes corresponding to the plurality of reference grid vertexes are obtained from the plurality of reference grid vertexes; determining a plurality of second reference grid vertices satisfying a second connection rule from the plurality of first reference grid vertices; and the plurality of reference grid vertexes are respectively connected with the plurality of second reference grid vertexes according to a second connection rule to obtain a reference surface patch set, so that the reference grids which do not meet the conditions can be conveniently removed from the plurality of reference grids, and meanwhile, the plurality of reference grids are further converted into the reference surface patches, so that the final reference surface patches not only contain side structure information and vertex information, but also contain surface structure information, the subsequent erroneous deletion operation in the process of deleting the surface patches which do not meet the requirements is avoided, the accuracy of the target surface patches which are finally reserved is ensured, and the accuracy of the conversion method of the point cloud model is further improved.

As an optional implementation manner, performing a target processing operation on a plurality of reference patches to obtain a target point cloud model, including:

S1, determining the shape of each patch corresponding to a plurality of reference patches according to the plurality of reference patches;

S2, determining a first panel set and a second panel set according to the determined shapes of the plurality of panels, wherein the first panel set comprises a plurality of first panels meeting preset splitting conditions, and the second panel set comprises a plurality of second panels except the plurality of first panels;

s3, respectively adopting a least square method for the plurality of first panels, and determining reference planes corresponding to the plurality of first panels respectively;

s4, determining fitting errors of each diagonal corresponding to the reference plane according to the vertex coordinates of the reference plane, and obtaining a plurality of fitting errors;

S5, splitting the reference plane according to the fitting errors to obtain a plurality of third panels;

And S6, determining a target point cloud model according to the third panels and the second panels.

It should be noted that, each of the plurality of reference patches in S1 corresponds to a patch shape, and the patch shape may be triangle, quadrangle, pentagon, or the like; the operation in S2 may be, but is not limited to, that the plurality of reference patches are classified according to the respective patch shapes of the plurality of reference patches into a plurality of first patches satisfying a preset splitting condition and a plurality of second patches not satisfying the preset splitting condition; the above-mentioned preset splitting condition may be, but is not limited to, pentagon, trapezoid, etc.

The operation in S3 may be, but is not limited to, understood as fitting a plane (i.e. the reference plane) by using a least square method according to the vertices of the corresponding patches of the first patches; the fitting error in S4 includes an angle error and a side length error, where the angle error is used to indicate a sum of errors of a plurality of internal angles and a preset angle of the surface patch, and the side length error is used to indicate a sum of errors of a plurality of side lengths and a preset length of the surface patch.

The operation in S6 may be, but is not limited to, understood as determining a final target point cloud model according to the splitting operation by using the obtained third panels and the second panels that are not split.

The operations in S1 to S6 described above can be understood as, but are not limited to: dough sheet setA small number of pentagonal panels need to be split into a quadrilateral panel and a triangular panel. Is provided with pentagonal panel/>Optionally/>Four vertexes in the plane/>, fitting the plane by using a least square methodAnd calculate fitting error/>Traversal/>Find the cause/>A minimum of four vertices, as shown in the following equation three:

Formula III

Wherein,Is the optional first/>, of four verticesCoordinate values of the vertices. Handle/>、/>、/>、Four vertexes are connected to form a quadrilateral surface sheet, and then the rest one vertex is connected with the vertex at/>Two adjacent vertexes of the three-dimensional triangle form another triangular surface patch.

The splitting operation in S5 may be, but not limited to, a first splitting operation and a second splitting operation, where the first splitting operation is as described above, and the shape of the pentagonal panel is set as required (the hexagonal panel may be split into the triangular panel and the pentagonal panel, or the hexagonal panel may be split into the two quadrilateral panels, or the heptagonal panel may be split into the triangular panel and the hexagonal panel, and so on), and the shape of the panel specifically split in the first splitting operation may be set as required.

After the first splitting operation is completed, for example, after the pentagonal panel is split into the triangular panel and the quadrilateral panel, a plurality of quadrilateral panels conforming to a second splitting condition are screened out from the non-split quadrilateral panel and the quadrilateral panel obtained by splitting, where the second splitting condition may be that internal angles of the quadrilateral exist which are not preset angles, for example, in the case that the irregular polygonal panel is the quadrilateral panel, the second splitting condition is that internal angles which are not preset angles (for example, 90 degrees) exist in four internal angles of the irregular quadrilateral panel, the screened out irregular quadrilateral panel is subjected to the second splitting operation, and when the second splitting operation is performed, the second splitting operation is performed on the irregular quadrilateral panel according to the determined diagonal line according to the angle error and the side length error corresponding to each of the plurality of irregular quadrilateral panels.

It is to be understood that the types of the patches targeted by the second splitting operation may be various, such as pentagonal patches, hexagonal patches, etc., the quadrilateral patches targeted by the second splitting operation are only one example of the present application, the present application does not specifically limit the types of the patches targeted by the first splitting operation and the second splitting operation (the types of the patches herein may be, but are not limited to, the number of sides of the patch shape), and, in the case where the types of the patches targeted by the first splitting operation and the second splitting operation include various types, the first splitting condition corresponding to the first splitting operation, and the second splitting condition corresponding to the second splitting operation may be plural, for example, the first splitting condition and the second splitting condition may be any one or a combination of plural of the first splitting condition and the second splitting condition, wherein the number of sides of the dough sheet shape is larger than the preset number of sides, the number of inner angles of the dough sheet shape is larger than the preset number of inner angles, the difference between the sum of the errors of the inner angles of the dough sheet shape and the preset angle error is larger than the preset difference, and the difference between the side of the sides of the dough sheet shape and the preset side error is larger than the preset side error

The above second splitting operation is, for example: in order to finally obtain regular quadrilateral patches, it is then necessary to split the irregular quadrilaterals in the set of patches into two triangular patches. Set quadrilateral dough sheet asFitting the four vertexes by using a least square method to obtain a plane/>And calculate fitting error/>Setting a threshold/>When/>When the following formula four is satisfied, the quadrilateral patch/>, is determinedNeeds to be split into two triangular patches:

Equation four

Wherein,For/>First of four vertices/>Coordinate values of the vertices.

Assuming quadrilateral patchesTwo diagonals/>、/>Respectively by/>And/>、/>And/>The four vertexes are connected and formed, and the distance/> between the far coordinate point and the two diagonals can be obtained through the following formula five and the formula six respectively、。

Formula five

Formula six

The parameters in the above formula five can be obtained from the following formula seven and formula eight:

Equation seven

Equation eight

The parameters in the above formula six can be obtained from the following formula nine and formula ten:

formula nine/>

Formula ten

If it isThen select diagonal/>Quadrilateral dough piece/>Splitting into two triangular patches, otherwise selecting/>To split. The pentagonal face and the irregular quadrilateral face in the face set F can be split by the method, the face pieces before splitting are shown in the (a) diagram in fig. 4, the face pieces after splitting are shown in the (b) diagram in fig. 4, and only the quadrilateral face piece and the triangular face piece in the face set F after splitting are shown in the (b) diagram in fig. 4.

With the above-described embodiment of the present application, the patch shapes corresponding to the respective reference patches are determined based on the plurality of reference patches; determining a first panel set and a second panel set according to the determined shapes of the plurality of panels, wherein the first panel set comprises a plurality of first panels meeting preset splitting conditions, and the second panel set comprises a plurality of second panels except the plurality of first panels; a least square method is adopted for the plurality of first panels respectively, and reference planes corresponding to the plurality of first panels are determined; according to the vertex coordinates of the reference plane, determining fitting errors of each diagonal corresponding to the reference plane, and obtaining a plurality of fitting errors; splitting the reference plane according to the fitting errors to obtain a plurality of third panels; and determining a target point cloud model according to the plurality of third panels and the plurality of second panels, classifying the panels, splitting the panels meeting splitting conditions, and facilitating finer determination of the panels which can be finally reserved, thereby improving the accuracy of a reconstruction method of the point cloud model.

As an alternative embodiment, after the splitting operation is performed on the reference plane according to the plurality of fitting errors, the method further includes:

S1, performing a surface patch screening operation on a plurality of second surface patches and a plurality of third surface patches to obtain a plurality of fourth surface patches, wherein the surface patches corresponding to the fourth surface patches are identical in shape;

S2, determining a plurality of angle errors corresponding to the fourth surface plates, wherein each fourth surface plate corresponds to one angle error, the angle error is used for indicating a plurality of inner angles and a preset length error of the fourth surface plate, and the side length error is used for indicating the sum of a plurality of side lengths and the preset length error of the fourth surface plate;

S3, determining a plurality of side length errors corresponding to the fourth side pieces, wherein each fourth side piece corresponds to one side length error, and the side length errors are used for indicating the sum of the errors of the plurality of side lengths of the fourth side pieces and the preset length;

S4, determining a plurality of target patches from a plurality of fourth patches according to the plurality of angle errors and the plurality of side length errors.

The screening operation in S1 above may be understood as, but is not limited to: and screening out the patches with the preset shapes from the second patches and the third patches. The above-described preset shape is, for example, a quadrangle, a triangle, a pentagon, or the like (the embodiment of the present application is exemplified by the preset shape being a quadrangle).

The operations in S2 and S3 above may be, but are not limited to, understood as determining whether the screened preset-shaped dough piece satisfies the spraying condition (whether it is a sprayable dough piece) such as: in the case that the preset shape is a quadrangle, the spraying condition may be a regular quadrangle patch; in the case where the preset shape is a triangular dough sheet, the spraying condition may be a regular triangular dough sheet, etc., specific spraying conditions may be set in association with the preset shape, and both the preset shape and the spraying condition may be set in advance as needed, which is not limited herein.

The operation in S4 may be, but is not limited to, that is, the final sprayable target surface piece is further screened from the fourth surface pieces screened in S1 according to the judgment result of the fourth surface pieces in S2 to S3.

The operations in S1 to S4 described above are specifically, for example: because the subsequent path planning needs to take sprayable quadrilateral patches as input, sprayability analysis is needed to be carried out on the quadrilateral patches obtained after splitting, and patches meeting the digital camouflage spraying conditions are screened out for subsequent calculation. The path planning can be understood, but is not limited to, that a specific optimal spraying path is planned on an obtained target point cloud model (also referred to as a patch model), that is, the optimal paths are connected, and then parameters such as a motion track, a gesture and the like of the robot are obtained according to an optimal linking sequence, so that the robot can rapidly spray the sprayable part of the target object.

For any quadrilateral dough sheetFirst order its vertices, will/>The closest point row to the origin of coordinates among the four vertices of (2) is the first, use/>Representing that the distance/>, is found in the other three verticesThe nearest point is placed in the second place, use/>Representing that the distance/>, is found in the remaining two pointsThe nearest point is placed in the third place, use/>The remaining vertex is placed in the fourth position, use/>Representation at this time/>Expressed as: /(I)。

Assume that typical basic elements each using a digital camouflage are preset asSpecifically, two judgment conditions are specified as follows, when square dough sheet/>When all are coincident,/>The spray-coated dough sheet is obtained.

Judging condition one: quadrilateral dough sheetWhether the sum of absolute value errors of all interior angles and standard right angles is at a given threshold/>And (3) inner part.

According toThe four vertexes in the formula (1) are calculated to judge whether the following formula (eleven) is satisfied, if so, the formula (eleven) is satisfiedMeets the first judgment condition.

Formula eleven

In the above formula elevenIs/>/>The internal angle can be obtained from the following formula twelve:

Formula twelve

Wherein,Representation by vertex/>Direction/>Vector (e.g./>, in equation twelve above)Representation by vertex/>Direction/>The vector of (c) can be obtained by thirteen of the following formulas:

Formula thirteen

Judging condition II: quadrilateral dough sheetAll side lengths and/>Whether the sum of errors is at a given threshold/>And (3) inner part.

Bonding ofThe length/>, of four sides of the patch is calculated by using the coordinates of 4 vertexes and the following formula fourteen、、/>、/>。

Formula fourteen

Judging whether the following formula fifteen is satisfied, if so, thenThe judgment condition II is satisfied.

Formula fifteen

In the above formula fifteen, when 1, 2, 3, and 4 are respectively used,Respectively represent/>、/>、/>、/>。

The target point cloud model which only finally comprises the sprayable surface patch (namely the target surface patch) can be obtained through the two judging conditions, and it is to be noted that in order to further improve the accuracy of the reconstruction method of the point cloud model, after the execution of the steps S1 to S4, the operation of man-machine interaction for repairing the surface patch can be performed: in the actual spraying process, some areas (such as a front windshield glass and the like) of a target object (such as a vehicle to be sprayed) do not need to be sprayed, but the areas generate point clouds when a laser scanner scans a point cloud model, and the areas possibly can be used as areas to be sprayed for facing in subsequent processing, so that the sprayable facing can be recalibrated into a non-sprayable facing in a man-machine interaction manner, and unnecessary spraying operations can be reduced. Or removing the non-sprayable part of the target object in the process of acquiring the point cloud data of the target object (namely, the stage of establishing the reference point cloud model by point cloud scanning), and finally obtaining the reference point cloud model only comprising the sprayable part of the target object.

According to the embodiment of the application, after splitting the reference plane according to a plurality of fitting errors, carrying out a patch screening operation on a plurality of second patches and a plurality of third patches to obtain a plurality of fourth patches, wherein the patches corresponding to the fourth patches have the same shape; determining a plurality of angle errors corresponding to the fourth surface plates, wherein each fourth surface plate corresponds to one angle error, and the angle error is used for indicating the sum of a plurality of internal angles of the fourth surface plates and errors of preset angles; determining a plurality of side length errors corresponding to the fourth surface plates, wherein each fourth surface plate corresponds to one side length error, and the side length errors are used for indicating the sum of the errors of the plurality of side lengths of the fourth surface plates and the preset length; according to the angle errors and the side length errors, a plurality of target patches are determined from a plurality of fourth patches, firstly, patches conforming to the preset shape are screened from the patches according to the shapes of the patches, and further, from the patches conforming to the shapes, the patches which can be suitable for camouflage spraying can be accurately extracted from a patch set according to the first judgment condition (namely the angle error) and the second judgment condition (namely the side length error), so that the accuracy of the point cloud model reconstruction method is improved.

As an alternative embodiment, before acquiring the reference point cloud model of the target object, the method further includes:

S1, acquiring point cloud data of a target object;

S2, determining bounding boxes corresponding to the point cloud data according to the position information of each of a plurality of first data points in the point cloud data, wherein the bounding boxes are used for indicating the optimal bounding space of the point cloud data;

S3, splitting the bounding box according to a preset size to obtain a plurality of split shapes;

S4, deleting the split shape without the second data point in the split shapes to obtain a plurality of reference shapes, wherein the second data point is any one of a plurality of first data points;

and S5, determining a plurality of reference data points corresponding to the plurality of reference shapes according to the first data points respectively included in the plurality of reference shapes, and obtaining a reference point cloud model including the plurality of reference shapes.

It should be noted that, the point cloud data in S1 may be, but not limited to, point cloud data obtained by performing and multi-azimuth scanning on a target object (for example, a terminal) by using a target device, where the target object is, for example, a vehicle to be sprayed, a garment to be sprayed, or the like.

The following describes an example of the method for reconstructing the point cloud model by taking a target object as a vehicle to be sprayed in combination with fig. 5:

in the point cloud data of the vehicle to be sprayed, the number of data points is about millions, so that the efficiency of the reconstruction algorithm is seriously affected by the large number of data points, therefore, the data point set (i.e. the point cloud data) needs to be simplified so as to be capable of being reconstructed later But also other sizes, the application is not limited to adopting a grid based on/>The side-length square region simplification method (i.e., S2 to S5 above) is as follows:

firstly, a three-dimensional coordinate system is established according to the requirement, and the minimum value of the point cloud data on X, Y, Z coordinate axes of the three-dimensional coordinate system is calculated 、/>、/>Sum maximum/>、/>、/>(I.e., maximum and minimum values in respective coordinates corresponding to a plurality of data points in a point cloud) to obtain a bounding box parallel to the coordinate axes, which may be, but is not limited to, a minimum bounding box of an initial point cloud model formed by point cloud data of a vehicle to be sprayed (the bounding box may be an OBB bounding box, and an OBB minimum bounding box algorithm is an algorithm for calculating a minimum bounding box of an object in a three-dimensional space). With side length SE (here/>) Uniformly dividing the bounding box by the square, wherein the obtained square set is shown in the following formula sixteen:

Sixteen formulas

Each cube in the above formula sixteenCan be represented by the following formula seventeen, wherein/>、/>、/>Respectively, the index numbers of the cube on the X, Y, Z coordinate axes, which can be understood by taking the origin of coordinates as the starting position, and the arrangement position of the cube on the X, Y, Z coordinate axes (for example, cube/>Is on the X-axis/>Cube, and is on Y-axis/>Cube, and is on X-axis/>Cube) the maximum value of the index number of the cube on X, Y, Z three axes can be determined by eighteen of the following formulas:

Formula seventeen

Equation eighteen

Collecting cubesAll cubes in (3) are arranged on each axis according to the X, Y, Z axis sequence and the index respectively, so that the aggregation/>, of each cube is determinedNumber/>The above arrangement according to the index may be understood as, but not limited to, comparing the numbers of cubes on the X axis first, then comparing the numbers of cubes on the Y axis, and finally comparing the numbers of cubes on the Z axis. The above-mentioned arrangement is performed according to the index, for example, comparing cubes a and B, and in the case that the serial numbers of a and B on the X axis are different, determining the numbers of a and B according to the magnitude of the serial numbers of a and B on the X axis; comparing the magnitudes of the serial numbers of A and B on the Y axis under the condition that the serial numbers of A and B on the X axis are the same; under the condition that the serial numbers of A and B on the Y axis are different, the serial numbers of A and B are determined according to the serial numbers of A and B on the Y axis; and under the condition that the serial numbers of A and B on the Y axis are the same, comparing the serial numbers of A and B on the Z axis, and determining the serial numbers of A and B according to the serial numbers of A and B on the Z axis.

Set the first point in the point cloudThe individual points are/>The/>, can be calculated by the following formula nineteenNumber of located cube/>. Traversing a plurality of data points in the point cloud data, finding cubes corresponding to each point, and collecting/>, in the cubesThe cube corresponding to the non-point is deleted to obtain the set/>。

Nineteenth formula

AggregationThe cube of (a) in fig. 5 generally contains a plurality of data points, which need to be reduced to one point (such as reference data point 507 in fig. 5 (a), reference data point 508 in fig. 5 (b), and reference data point 509 in fig. 5 (c)) to represent the cube (reference data point 507 in fig. 5 (a), reference data point 508 in fig. 5 (b), cube in which reference data point 508 is shown, and cube in which reference data point 509 is shown in fig. 5 (c)). As shown in fig. 5 (a) to (c), the same area is simplified by different simplification methods, and the simplified points are connected to obtain an effect diagram. The above collection/>Such as reference shape 501 shown in fig. 5 (a), reference shape 502 shown in fig. 5 (b), and reference shape 503 shown in fig. 5 (c); each cube described above contains a plurality of data points such as data point 504 shown in fig. 5 (a), data point 505 shown in fig. 5 (b), and data point 506 shown in fig. 5 (c).

Assuming a cubeFor the collection/>Any one cube of the three-dimensional structure, the existing simplification method is mainly divided into two types: the first simplified method (effect is shown in fig. 5 (a)) is: cube/>Neutral cube/>The data point with the nearest center point is used as a simplified point, and the method can obtain the simplified points with even distribution, but cannot accurately reflect the distribution condition of the data points; the second simplified method (effect is shown in fig. 5 (b)) is: cube/>The average value of all data points in the data points is taken as a simplified point, and the method can accurately show the distribution condition of the data points, but the distribution of the simplified points is uneven. Thus in order to embody the cube/>The distribution condition of medium data points can ensure the uniformity degree of simplified points, and the application adopts a weighted average method to obtain cubes/>Simplified dot/>(Effect is shown in (c) diagram in FIG. 5), simplified dot/>Can be determined according to the following equation twenty: /(I)

Formula twenty

In the above formula twentyFor/>Cube center point of/>Representation/>Is provided with a set of data points in the database,For/>Number of data points.

According to the embodiment of the application, the point cloud data of the target object is acquired before the reference point cloud model of the target object is acquired; determining bounding boxes corresponding to the point cloud data according to the position information of each of the plurality of first data points in the point cloud data, wherein the bounding boxes are used for indicating the optimal bounding space of the point cloud data; deleting the split shape without the second data point in the split shapes to obtain a plurality of reference shapes, wherein the second data point is any one of a plurality of first data points; according to the first data points included in the reference shapes, the reference data points corresponding to the reference shapes are determined, the reference point cloud model including the reference shapes is obtained, the number of the data points included in the point cloud data can be greatly reduced, the calculation amount of unnecessary data points in the reconstruction process of the point cloud model is reduced, and the reconstruction efficiency of the point cloud model is further improved.

The following describes the overall method for reconstructing the point cloud model with reference to fig. 6:

S602, inputting point cloud data: the method comprises the steps that a target object is scanned in multiple directions (at different angles) through scanning equipment or a terminal to obtain point cloud data which corresponds to the target object and comprises a plurality of data points;

s604, data point simplification: the efficiency of the reconstruction method of the point cloud model is seriously affected by a large number of data points, so that the large number of data points are required to be simplified to obtain relatively fewer data points, and the original point cloud model formed by the data points before the simplification is not changed;

S606, grid model generation: the plurality of cubes obtained in S604 (i.e., the cube set All the cubes included in the grid model (which may be, but is not limited to, a plurality of reference grids included in the grid model) are generated by connecting simplified points of adjacent cubes according to a certain rule (i.e., the first connection rule) in the step S604, since the cubes are ordered according to the index number of each cube, and thus, adjacent cubes of each cube can be determined;

s608, patch extraction (may also be referred to as patch extraction): a plurality of reference grids obtained by the operation in the above-described S606 (i.e., the above-described grid set The multiple reference meshes included in) contain only edge structure and vertex information, and do not contain structure information of faces, so it is necessary to collect/>, from the meshesThe regular square dough piece and the regular triangular dough piece are obtained by processing the dough piece information;

S610, splitting the dough sheet: the splitting of the dough sheet (i.e., the splitting operation described above) includes a first splitting operation, which may be understood as, but is not limited to, splitting the dough sheet that satisfies the first splitting condition among the plurality of dough sheets generated in S608, and a second splitting operation, which may be understood as, but is not limited to, splitting the plurality of dough sheets obtained by the first splitting operation and the dough sheet that is not split, and that satisfies the second splitting condition, it may be understood that the second splitting operation may also be a secondary splitting of the dough sheet obtained by the first splitting operation, for example: the first splitting operation is to split the pentagonal panel into a triangular panel and a quadrangular panel, and the second splitting operation is to split the quadrangular panel obtained by the first splitting operation into two triangular panels;

s612, extracting the sprayable dough sheet: namely, the target surface piece (namely, the target surface piece) meeting the spraying condition is screened from the surface pieces (namely, the third surface pieces) obtained through the splitting operation and the surface pieces (namely, the second surface pieces) which are not split;

s614, repair face sheet: the operation may be performed after the operation of extracting the sprayable dough sheet in S612 or performed in the processes in S602 to S604;

S616, a dough sheet model: the patch model including a plurality of target patches (i.e., the target point cloud model) can be obtained through S602 to S614.

According to the reconstruction method of the point cloud model, the efficiency of the reconstruction algorithm is improved through simplification of the point cloud data points, so that the efficiency of the reconstruction method of the point cloud model is improved, and meanwhile, in the specific reconstruction process of the point cloud model (namely S604 to S614), the accuracy of a plurality of target patches included in the target point cloud model is ensured through operations such as splitting and screening of the reference point cloud model, so that the accuracy of the reconstruction method of the point cloud model is improved.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.

According to another aspect of the embodiment of the present invention, there is also provided a reconstruction apparatus of a point cloud model for implementing the above reconstruction method of a point cloud model, as shown in fig. 7, the apparatus includes:

An obtaining unit 702, configured to obtain a reference point cloud model of a target object, where the reference point cloud model includes a plurality of reference shapes, the plurality of reference shapes corresponding to a plurality of reference data points, and each of the reference shapes corresponds to one of the reference data points;

A determining unit 704, configured to determine a mesh model corresponding to the reference point cloud model according to the reference point cloud model, where the mesh model includes a plurality of reference meshes, the plurality of reference meshes are a plurality of meshes obtained by connecting the plurality of reference data points in different connection manners according to a first connection rule, and each of the plurality of reference meshes corresponds to a plurality of reference mesh vertices;

A connection unit 706, configured to connect the plurality of reference grid vertices according to a second connection rule to obtain a reference patch set, where the reference patch set includes a plurality of reference patches;

And a processing unit 708, configured to perform a target processing operation on the plurality of reference patches to obtain a target point cloud model, where the target point cloud model includes a plurality of target patches, the plurality of target patches correspond to a plurality of patch shapes one by one, and the plurality of patch shapes are patches with a plurality of preset shapes.

The specific manner in which the units of the above embodiments of the apparatus perform their operations has been described in detail in relation to the embodiments of the method, and will not be described in detail here.

According to still another aspect of the embodiment of the present invention, there is further provided an electronic device for implementing the above-mentioned method for reconstructing a point cloud model, where the electronic device may be a terminal device or a server as shown in fig. 8. The present embodiment is described taking the electronic device as a terminal device as an example. As shown in fig. 8, the electronic device includes: at least one processor 804; and a memory 802 communicatively coupled to the at least one processor 804; wherein the memory 802 stores a computer program executable by the at least one processor 804, the computer program being executable by the at least one processor 804 to cause the at least one processor 804 to perform the steps of any one of the method embodiments described above.

Alternatively, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of the computer network.

Alternatively, in this embodiment, the above-mentioned processor may be configured to execute the following steps by a computer program:

S1, acquiring a reference point cloud model of a target object, wherein the reference point cloud model comprises a plurality of reference shapes, the reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one reference data point;

S2, determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are obtained by connecting a plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively;

S3, connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches;

And S4, performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches are in one-to-one correspondence with the plurality of patch shapes, and the plurality of patch shapes are patches with a plurality of preset shapes.

Alternatively, it will be understood by those skilled in the art that the structure shown in fig. 8 is only schematic, and the electronic device may also be a terminal device such as a smart phone (e.g. an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, and a Mobile internet device (Mobile INTERNET DEVICES, MID), a PAD, etc. Fig. 8 is not limited to the structure of the electronic device described above. For example, the electronic device may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 8, or have a different configuration than shown in FIG. 8.

The memory 802 may be used to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for reconstructing a point cloud model in the embodiment of the present invention, and the processor 804 executes the software programs and modules stored in the memory 802, thereby executing various functional applications and data processing, that is, implementing the method for reconstructing a point cloud model. Memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 802 may further include memory remotely located relative to processor 804, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 802 may be used to store, but is not limited to, point cloud data and information such as a reference point cloud model and a target point cloud model. As an example, as shown in fig. 8, the memory 802 may include, but is not limited to, an acquisition unit 702, a determination unit 704, a connection unit 706, and a processing unit 708 in a reconstruction apparatus including the point cloud model. In addition, other module units in the reconstruction device of the point cloud model may be further included, but are not limited to, and are not described in detail in this example.

Optionally, the transmission device 806 is used to receive or transmit data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission means 806 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices and routers via a network cable to communicate with the internet or a local area network. In one example, the transmission device 806 is a Radio Frequency (RF) module for communicating wirelessly with the internet.

In addition, the electronic device further includes: a display 808, and a connection bus 810 for connecting the various module components in the electronic device described above.

In other embodiments, the terminal device or the server may be a node in a distributed system, where the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the plurality of nodes through a network communication. Among them, the nodes may form a Peer-To-Peer (P2P) network, and any type of computing device, such as a server, a terminal, etc., may become a node in the blockchain system by joining the Peer-To-Peer network.

According to one aspect of the present application, there is provided a computer program product comprising a computer program/instruction containing program code for executing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. When executed by a central processing unit, performs various functions provided by embodiments of the present application.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

According to an aspect of the present application, there is provided a computer readable storage medium, from which a processor of a computer device reads the computer instructions, the processor executing the computer instructions, so that the computer device performs the above-described reconstruction method of a point cloud model.

Alternatively, in the present embodiment, the above-described computer-readable storage medium may be configured to store a computer program for performing the steps of:

Those skilled in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by computer programs to instruct related hardware, and the programs may be stored in a computer readable storage medium, which when executed may include the above-described method embodiments. The storage medium may be a magnetic disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a Flash Memory (FM), a hard disk (HARD DISK DRIVE HDD), or a Solid state disk (Solid-STATE DRIVE SSD); the storage medium may also comprise a combination of memories of the kind described above.

The units integrated in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the above-described method of the various embodiments of the present invention.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In several embodiments provided by the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the above, is merely a logical function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. The reconstruction method of the point cloud model is characterized by comprising the following steps of:

acquiring a reference point cloud model of a target object, wherein the reference point cloud model comprises a plurality of reference shapes, the plurality of reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one reference data point respectively;

Determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are a plurality of grids obtained by connecting the plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively;

Connecting the plurality of reference grid vertices according to a second connection rule to obtain a reference patch set, wherein the reference patch set comprises a plurality of reference patches;

And performing target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches are in one-to-one correspondence with a plurality of patch shapes, and the plurality of patch shapes are patches with a plurality of preset shapes.

2. The method of claim 1, wherein determining a mesh model corresponding to the reference point cloud model from the reference point cloud model comprises:

acquiring adjacent relations among the plurality of reference shapes in the reference point cloud model;

Connecting the reference data points corresponding to the reference shapes according to the adjacent relation among the reference shapes in the reference point cloud model to obtain a grid model corresponding to the reference point cloud model, wherein the adjacent relation comprises the following steps: any one or a combination of a plurality of surface adjacency, edge adjacency and point adjacency.

3. The method of claim 2, wherein connecting the reference data points corresponding to each of the plurality of reference shapes according to the neighboring relationship between the plurality of reference shapes in the reference point cloud model to obtain a mesh model corresponding to the reference point cloud model comprises:

Acquiring a plurality of first reference shapes corresponding to the plurality of reference shapes respectively, wherein each reference shape and the plurality of first reference shapes have a plurality of adjacent relations, and each reference shape and each first reference shape have a single adjacent relation respectively;

Selecting a plurality of second reference shapes meeting preset conditions from the plurality of first reference shapes according to preset priorities of the plurality of adjacent relations;

and connecting the reference data points of the plurality of reference shapes with the reference data points corresponding to the plurality of second reference shapes to obtain a grid model corresponding to the reference point cloud model.

4. The method of claim 1, wherein connecting the plurality of reference mesh vertices according to a second connection rule results in a set of reference patches, comprising:

acquiring a plurality of first reference grid vertexes corresponding to the plurality of reference grid vertexes from the plurality of reference grid vertexes;

determining a plurality of second reference grid vertices from the plurality of first reference grid vertices that satisfy the second connection rule;

And respectively connecting the plurality of reference grid vertexes with the plurality of second reference grid vertexes according to a second connection rule to obtain a reference surface patch set.

5. The method of claim 1, wherein performing a target processing operation on the plurality of reference patches to obtain a target point cloud model comprises:

Determining the shape of each of the reference patches according to the reference patches;

determining a first panel set and a second panel set according to the determined shapes of the plurality of panels, wherein the first panel set comprises a plurality of first panels meeting a preset splitting condition, and the second panel set comprises a plurality of second panels except the plurality of first panels;

a least square method is adopted for the plurality of first panels, and reference planes corresponding to the plurality of first panels are determined;

according to the vertex coordinates of the reference plane, determining fitting errors of each diagonal corresponding to the reference plane, and obtaining fitting errors;

splitting the reference plane according to the fitting errors to obtain a plurality of third panels;

And determining the target point cloud model according to the third panels and the second panels.

6. The method of claim 5, wherein after splitting the reference plane based on a plurality of the fitting errors, the method further comprises:

Performing a patch screening operation on the second patches and the third patches to obtain a plurality of fourth patches, wherein the patches corresponding to the fourth patches have the same shape;

determining a plurality of angle errors corresponding to the fourth surface plates, wherein each fourth surface plate corresponds to one angle error, and the angle errors are used for indicating the sum of errors of a plurality of inner angles of the fourth surface plates and a preset angle;

Determining a plurality of side length errors corresponding to the fourth surface plates, wherein each fourth surface plate corresponds to one side length error, and the side length error is used for indicating the sum of the errors of the plurality of side lengths of the fourth surface plate and a preset length;

and determining a plurality of target patches from the fourth patches according to the plurality of angle errors and the plurality of side length errors.

7. The method of claim 1, wherein prior to obtaining the reference point cloud model of the target object, the method further comprises:

Acquiring point cloud data of the target object;

determining a bounding box corresponding to the point cloud data according to the position information of each of a plurality of first data points in the point cloud data, wherein the bounding box is used for indicating the optimal bounding space of the point cloud data;

splitting the bounding box according to a preset size to obtain a plurality of splitting shapes;

Deleting the split shape without the second data point in the split shapes to obtain the reference shapes, wherein the second data point is any one of the first data points;

and determining the plurality of reference data points corresponding to the plurality of reference shapes according to the first data points respectively included in the plurality of reference shapes, and obtaining a reference point cloud model including the plurality of reference shapes.

8. A point cloud model reconstruction device, comprising:

An obtaining unit, configured to obtain a reference point cloud model of a target object, where the reference point cloud model includes a plurality of reference shapes, the plurality of reference shapes correspond to a plurality of reference data points, and each reference shape corresponds to one of the reference data points;

The determining unit is used for determining a grid model corresponding to the reference point cloud model according to the reference point cloud model, wherein the grid model comprises a plurality of reference grids, the plurality of reference grids are a plurality of grids obtained by connecting the plurality of reference data points in different connection modes according to a first connection rule, and each reference grid in the plurality of reference grids corresponds to a plurality of reference grid vertexes respectively;

The connection unit is used for connecting the plurality of reference grid vertexes according to a second connection rule to obtain a reference surface patch set, wherein the reference surface patch set comprises a plurality of reference surface patches;

The processing unit is used for carrying out target processing operation on the plurality of reference patches to obtain a target point cloud model, wherein the target point cloud model comprises a plurality of target patches, the plurality of target patches are in one-to-one correspondence with the plurality of patch shapes, and the plurality of patch shapes are patches with a plurality of preset shapes.

9. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing a computer to perform the method of reconstructing a point cloud model according to any one of claims 1 to 7.

10. An electronic device, the electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to cause the at least one processor to perform the method of reconstructing a point cloud model according to any one of claims 1-7.