CN116030103A

CN116030103A - Method, device, apparatus and medium for determining masonry quality

Info

Publication number: CN116030103A
Application number: CN202310219815.8A
Authority: CN
Inventors: 姜禾; 卢飞翔; 李龙腾; 张良俊
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2023-03-07
Filing date: 2023-03-07
Publication date: 2023-04-28
Anticipated expiration: 2043-03-07
Also published as: CN116030103B

Abstract

The disclosure provides a method, a device, equipment and a medium for determining masonry quality, relates to the field of artificial intelligence, and particularly relates to the technical fields of computer vision, construction quality and the like. The concrete implementation scheme of the method for determining the masonry quality is as follows: projecting each wall surface to a two-dimensional plane based on point cloud data aiming at the wall surface to be detected to obtain a two-dimensional image of each wall surface; determining actual depth information of each wall surface based on target point cloud data in the point cloud data of the wall body to be detected; the target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection; and determining the masonry quality of the wall to be tested according to the difference between the actual depth information of each wall surface and the theoretical depth information of each wall surface, wherein the theoretical depth information is determined based on a preset three-dimensional model of the wall to be tested.

Description

Method, device, apparatus and medium for determining masonry quality

Technical Field

The present disclosure relates to the field of artificial intelligence, and in particular, to the technical fields of computer vision, construction measurement, and the like, and more particularly, to a method, apparatus, device, and medium for determining masonry quality.

Background

With the development of computer technology and electronic technology, artificial intelligence and robotics are applied in more and more fields. However, the traditional building field usually adopts a lagging construction means and mode, for example, the assessment of the building masonry quality of a building is mainly realized by manually using a hand tool, a spring wire and the like, and the problems that the assessment precision cannot be ensured and the labor cost is higher and higher exist.

Disclosure of Invention

The present disclosure is directed to a method, apparatus, electronic device, and storage medium for determining masonry quality that facilitate improved evaluation accuracy, reduced labor costs.

According to one aspect of the present disclosure, there is provided a method of determining masonry quality, comprising: projecting each wall surface to a two-dimensional plane based on point cloud data aiming at the wall surface to be detected to obtain a two-dimensional image of each wall surface; determining actual depth information of each wall surface based on target point cloud data in the point cloud data of the wall body to be detected; the target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection; and determining the masonry quality of the wall to be tested according to the difference between the actual depth information of each wall surface and the theoretical depth information of each wall surface, wherein the theoretical depth information is determined based on a preset three-dimensional model of the wall to be tested.

According to another aspect of the present disclosure, there is provided an apparatus for determining masonry quality, comprising: the projection module is used for projecting each wall surface to a two-dimensional plane based on the point cloud data of the wall body to be detected aiming at each wall surface in at least one wall surface included in the wall body to be detected, so as to obtain a two-dimensional image of each wall surface; the depth determining module is used for determining the actual depth information of each wall surface based on the target point cloud data in the point cloud data of the wall body to be detected; the target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection; and the masonry quality determining module is used for determining the masonry quality of the wall to be tested according to the difference between the actual depth information of each wall surface and the theoretical depth information of each wall surface, wherein the theoretical depth information is determined based on a preset three-dimensional model of the wall to be tested.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of determining masonry quality provided by the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of determining masonry quality provided by the present disclosure.

According to another aspect of the present disclosure, there is provided a computer program product comprising computer programs/instructions stored on at least one of a readable storage medium and an electronic device, which when executed by a processor, implement the method of determining masonry quality provided by the present disclosure.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic illustration of an application scenario of a method and apparatus for determining masonry quality according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method of determining masonry quality according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of the principle of acquiring point cloud data according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of determining wall point cloud data for a wall to be tested according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of two-dimensional projection of point cloud data according to an embodiment of the present disclosure;

FIG. 6 is a schematic illustration of the effect of embodying masonry quality in accordance with an embodiment of the present disclosure;

FIG. 7 is a block diagram of an apparatus for determining masonry quality according to an embodiment of the present disclosure; and

fig. 8 is a block diagram of an electronic device for implementing a method of determining masonry quality in accordance with an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The traditional building industry faces a plurality of constraints such as lagged construction means and modes, labor waste, ageing of workers, rising labor cost and the like. With the development of artificial intelligence and robot technology, it is expected that workers in the construction industry can be released from traditional construction operations and environments with the characteristics of danger, propagation, dirtiness and heavy by means of the technology so as to perform work requiring more manual operations.

To address this problem, the present disclosure provides a method, apparatus, device, and medium for determining masonry quality. An application scenario of the method and apparatus provided in the present disclosure is described below with reference to fig. 1.

Fig. 1 is a schematic view of an application scenario of a method and apparatus for determining masonry quality according to an embodiment of the present disclosure.

As shown in fig. 1, the application scenario 100 Of this embodiment may include an acquisition device 110, where the acquisition device 110 may be, for example, a 3D scanning device, and may specifically include a laser radar, an RGB binocular camera, a 3D structured light camera, or a Time-Of-flight camera (TOF camera), or the like. The acquisition device 110 may be used, for example, to acquire point cloud data for any object in its environment.

In an embodiment, when the masonry quality of the wall 120 needs to be evaluated, for example, the acquisition device 110 may be installed directly in front of the wall 120 or at any position around the wall 120 according to the requirement, so as to acquire the point cloud data of the wall 120 by means of the acquisition device 110. The acquisition device 110 may also be connected to the electronic device 130 through wired or wireless communication, for example, to transmit the acquired point cloud data to the electronic device 130, so as to process the acquired point cloud data by the electronic device. The installation position of the collecting device 110 may be determined according to, for example, an evaluation requirement of masonry quality, and the present disclosure is not limited thereto.

The electronic device 130 may be, for example, various electronic devices with processing capabilities including, but not limited to, a laptop portable computer, a desktop computer, a server, and the like. For example, various client applications may also be running in the electronic device 130, such as a three-dimensional modeling class application, a data processing class application, a quality assessment class application, a cloud platform class application, and the like (just examples).

In an embodiment, the electronic device 130 may obtain the actual depth information of each wall surface in the wall 120 by processing the point cloud data of the wall 120 acquired by the acquisition device 110, for example. And the comparison result representing the masonry quality 160 is obtained by comparing the actual depth information with the theoretical depth information of each wall surface. Here, the theoretical depth information may be obtained in response to an input operation of a user, for example, or may be obtained according to a three-dimensional model 140 previously constructed for the wall 120, which is not limited in the present disclosure.

In an embodiment, the application scenario 100 may further comprise a server 150. The server 150 may be any type of server, such as a database server, a cloud server, or a blockchain server, for example, and the server 150 may be a background management server for supporting the running of client applications installed in the electronic device 130, which is not limited in this disclosure. For example, the electronic device 130 may be communicatively connected to the server 150, for example, through a network, to obtain the three-dimensional model 140 pre-built for the wall 120 from the server 150, thereby obtaining theoretical depth information of the wall 120.

In an embodiment, the electronic device 130 may also send the point cloud data collected by the collection device 110 for the wall 120 to the server 150, and the server 150 processes the point cloud data, and compares the actual depth information of each wall in the wall 120 with the theoretical depth information to obtain a comparison result representing the masonry quality 160.

It should be noted that, the method for determining masonry quality provided in the present disclosure may be performed by the electronic device 130 or may be performed by the server 150. Accordingly, the apparatus for determining masonry quality provided by the present disclosure may be disposed in the electronic device 130 or in the server 150.

It should be understood that the number and type of electronic devices 130, walls 120, and servers 150 in fig. 1 are merely illustrative. There may be any number and type of electronic devices 130, walls 120, and servers 150 as desired for an implementation.

The method of determining masonry quality provided by the present disclosure will be described in detail below in conjunction with fig. 2-6.

Fig. 2 is a flow chart of a method of determining masonry quality according to an embodiment of the present disclosure.

As shown in fig. 2, the method 200 of determining masonry quality of this embodiment may include operations S210 to S230.

In operation S210, for each of at least one wall surface included in the wall to be measured, each wall surface is projected to a two-dimensional plane based on the point cloud data for the wall to be measured, and a two-dimensional image of each wall surface is obtained.

According to an embodiment of the disclosure, the point cloud data for the wall to be detected may be, for example, the point cloud data collected by the collection device, which is arranged right in front of the wall to be detected in the wall to be detected. When the wall surface to be detected is a plurality of wall surfaces, a group of point cloud data can be acquired for each wall surface, and the three-dimensional point cloud data is converted into a two-dimensional image for the group of point cloud data acquired for each wall surface, so that the two-dimensional image of each wall surface is obtained.

For example, three-dimensional point cloud data (x, y, z) may be converted into two-dimensional pixel points (u, v) according to a mapping relationship between the point cloud and the image, and then image drawing is performed according to the two-dimensional pixel points, so as to obtain a two-dimensional image of each wall surface.

For example, when obtaining a two-dimensional image of each wall surface, point Cloud Data (PCD) may be read by using a Point Cloud library (Point Cloud Library), and an OpenCV library may be called, and coordinate values representing the depth of the wall surface in a three-dimensional coordinate system may be transformed to obtain a gray value of each pixel Point in the image, where the gray value represents a depth value corresponding to each pixel Point. Thus, each pixel in the projected two-dimensional image has a corresponding depth value.

For example, openGL (Open Graphics Library) may be used to perform coordinate transformation on the point cloud data, so as to obtain two-dimensional data after projection of the point cloud data, and obtain a two-dimensional image of each wall surface. Specifically, for example, orthographic projection transformation may be used to convert three-dimensional point cloud data into data in a two-dimensional coordinate system, so as to obtain a two-dimensional image. It can be appreciated that in the orthographic projection transformation process, for example, the point cloud data may be transformed via a projection matrix to obtain a clipping coordinate value in a clipping space corresponding to the point cloud data, where the clipping coordinate is homogeneous coordinate. Then, clipping the point cloud data in clipping space, performing perspective division processing on the clipped point cloud to obtain point cloud data transformed into a standardized equipment coordinate system (Normalized Device Coordinates, abbreviated as NDC), and performing view port transformation on the point cloud data transformed into the NDC coordinate system to obtain coordinate values of pixels corresponding to the two-dimensional image, thereby obtaining the two-dimensional image of each wall surface.

For example, the acquired point cloud data under the coordinate system constructed based on the acquisition equipment can be converted into the world coordinate system according to the internal parameters and external parameters of the acquisition equipment, then the point cloud data under the world coordinate system is used as the input of a WorldToViewportPoint () tool, and the tool returns the view port position where the two-dimensional pixel point corresponding to the point cloud data is located, the depth information of the wall point corresponding to the two-dimensional pixel point and the like.

According to the embodiment of the disclosure, when the point cloud data of the wall is projected, for example, plane fitting can be performed on the point cloud data of the wall to be detected to obtain a normal vector of each wall, and then the projection of the point cloud data is performed along the direction of the normal vector of each wall, so that a two-dimensional image of each wall is obtained.

In operation S220, the actual depth information of each wall surface is determined based on the target point cloud data in the point cloud data of the wall surface to be measured. The target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection.

In this embodiment, the target point cloud data is for each wall surface, and specifically, the target point cloud data for each wall surface may be the point cloud data for each wall surface. The embodiment can convert the point cloud data of each wall surface into a three-dimensional coordinate system constructed based on the wall surface, and then uses the coordinate value of each point cloud in the normal direction of the wall surface in the three-dimensional coordinate system constructed based on the wall surface as the actual depth information of the wall surface.

According to the embodiments of the present disclosure, the coordinate value of the point cloud data along the normal direction of each wall surface from the standardized apparatus coordinate system may be taken as the actual depth information of each wall surface. The depth image formed by the coordinate values of the point cloud data along the normal direction of each wall surface can also be used as the actual depth information of each wall surface.

In operation S230, the masonry quality of the wall to be measured is determined according to the difference between the actual depth information of each wall surface and the theoretical depth information of each wall surface.

The theoretical depth information is determined based on a preset three-dimensional model of the wall to be measured. The predetermined three-dimensional model of the wall to be measured may be, for example, a CAD model. For example, a depth image of each wall surface in the wall to be measured can be obtained according to a predetermined three-dimensional model, and the depth image is used as standard depth information. For example, the standard depth information may be obtained by performing projection similar to operation S210 on each surface point of the predetermined three-dimensional model, using a principle similar to that of obtaining the actual depth information.

The embodiment can compare the depth image representing the actual depth information of each wall surface with the depth image representing the standard depth information of each wall surface by pixel depth values, and takes the difference value of the depth values as the difference between the actual depth information and the theoretical depth information. And comparing the pixel-by-pixel depth values according to the two-dimensional image of each wall surface and the projection image of each wall surface in the CAD model, and taking the difference value of the depth values as the difference between the actual depth information and the theoretical depth information.

For example, this embodiment may represent the masonry quality of the wall to be tested from the difference between the actual depth information and the theoretical depth information. Alternatively, the embodiment may represent the masonry quality of the wall to be tested by the maximum value of the pixel-by-pixel depth value difference, the mean square error, etc. Alternatively, the embodiment may represent the masonry quality of the wall to be tested from a depth difference image representing the difference in pixel-by-pixel depth values. Alternatively, the embodiment may take the difference between the actual depth information and the theoretical depth information as an input to a deep learning model for quality evaluation obtained by training in advance, and output a score representing the masonry quality of the wall to be measured from the deep learning model, or the like. The deep learning model may be, for example, a CNN model, and the present disclosure is not limited thereto.

According to the embodiment of the disclosure, the point cloud data are subjected to two-dimensional projection, so that the wall point cloud in the wall can be conveniently corresponding to the wall points in the preset three-dimensional model; by the correspondence between the point cloud and the pixels in the projected two-dimensional image, the actual depth information can be obtained which can be compared with the theoretical depth information obtained from the predetermined three-dimensional model. Comprehensively, through the technical scheme of the embodiment of the disclosure, accurate comparison between the actual depth information and the theoretical depth information of the wall points is facilitated. Compared with the technical scheme that manual measurement is needed in the field to determine the masonry quality, the method can reduce the labor cost of masonry quality assessment and improve the assessment precision.

Fig. 3 is a schematic diagram of the principle of acquiring point cloud data according to an embodiment of the present disclosure.

According to the embodiment of the disclosure, the point cloud data of the wall to be detected can be acquired under at least two acquisition parameters, and then the point cloud data of the wall to be detected is obtained by splicing the point cloud data acquired under a plurality of acquisition parameters. Therefore, the situation that the acquired point cloud data is missing due to the fact that certain areas of the wall are invisible and blocked or due to the strong light reflection effect can be eliminated, and the integrity of the acquired point cloud data of the wall to be detected is improved.

According to the embodiment of the disclosure, the point cloud data can be acquired by, for example, projecting the coded structured light to the wall to be measured by a PhoXi3D scanner or the like under at least two acquisition parameters, and performing interpretation and reconstruction of the point cloud based on the received reflected light. That is, the acquisition device may employ a PhoXi3D scanner or the like, which is not limited by the present disclosure.

It is understood that at least two acquisition parameters for acquiring the point cloud data may be set, for example, according to the wall surface to be evaluated according to actual needs. For example, in the embodiment 300 shown in fig. 3, if an outer wall 310 of a wall having a "sailboat" pattern is to be evaluated, the at least two acquisition parameters may include at least two angles, or at least two heights, etc. that are directly in front of and toward the outer wall, which is not limiting to the present disclosure. For example, the image capturing may be performed by disposing the capturing devices at positions 301 to 303 as shown in fig. 3, and the capturing angles of the capturing devices may be the same or different at positions 301 to 303, for example, which is not limited in the present disclosure.

For example, at least two sets of point cloud data may be acquired under the at least two acquisition parameters, where the at least two sets of point cloud data may constitute original point cloud data acquired for the wall to be measured. For example, three sets of point cloud data acquired at locations 301 to 303 may be configured as the original point cloud data 320.

After obtaining the original point cloud data 320, the embodiment may down-convert at least two sets of point cloud data from different device coordinate systems to a unified target coordinate system, thereby obtaining the transformed point cloud data 330. The transformed point cloud data may then be spliced to obtain point cloud data 340 for the wall to be tested.

The target coordinate system may be, for example, any coordinate system preset according to actual requirements, which is not limited in the disclosure.

In an embodiment, for example, the reference object 350 may be fixedly disposed relative to the wall to be tested, the reference object 350 may be, for example, a checkerboard, a target, etc., the reference object 350 may be disposed parallel to the outer wall 310 to be evaluated, and may be fixed on the outer wall 310, and the location of the reference object 350 is not limited in this disclosure.

Since the wall to be measured and the reference 350 are stationary with respect to each other, this embodiment can use the coordinate system constructed based on the reference 350 as the target coordinate system. As such, this embodiment may also determine a transformation relationship between the coordinate system constructed for the acquisition device and the target coordinate system constructed for the reference object 350 based on the pre-calibrated relative positional relationship between the reference object 350 and the acquisition device. At least two sets of point cloud data are then transformed into a unified target coordinate system based on the transformation relationship, resulting in transformed point cloud data 330.

It will be appreciated that the determined transformation relationship is different for acquisition devices that acquire point cloud data at different acquisition parameters. For the acquisition equipment for acquiring the point cloud data under any acquisition parameter, if the center point of the acquisition equipment is set as the origin of the coordinate system constructed for the acquisition equipment, the direction of pointing to the outer wall surface 310 from the center point of the acquisition equipment vertically is set as the Z axis of the coordinate system constructed for the acquisition equipment, and the construction is satisfied to the rightThe coordinate system of the hand for the acquisition device is then based on the coordinate value (x) of the origin of the coordinates of the target coordinate system in the coordinate system constructed for the acquisition device ₀ ，y ₀ ，z ₀ ) The amount of translation t= (x) between the two coordinate systems can be obtained ₀ ，y ₀ ，z ₀ ) ^T . For example, according to the coordinate values of the plurality of feature points of the reference object 350 in the target coordinate system and the coordinate values of the reference object 350 in the coordinate system constructed for the acquisition device, the rotation matrix r transformed between the two coordinate systems is obtained by solving by using the principle of least square method indirect adjustment, for example, the rotation matrix r may be expressed as a matrix as shown in the following formula (1). The feature points of the reference object 350 may include, for example, a center point of the reference object 350, a contour point of the reference object 350, and the like, which is not limited in this disclosure. The above principles of solving the rotation matrix r are merely examples to facilitate understanding of the present disclosure, which is not limited thereto.

Wherein a is ₁₁ 、a ₁₂ 、a ₁₃ 、a ₂₁ 、a ₂₂ 、a ₂₃ 、a ₃₁ 、a ₃₂ 、a ₃₃ All are parameters obtained by solving the principle of least square method indirect adjustment. In this way, the transformation relation T between the coordinate system constructed for the acquisition apparatus and the target coordinate system can be expressed as the following formula (2), for example.

According to an embodiment of the present disclosure, the stitching of point cloud data refers to a process in which overlapping portions of point cloud data at arbitrary positions are registered with each other. When the transformed point cloud data are spliced, for example, a point cloud registration algorithm such as an iterative nearest point algorithm (Iterative Closest Point, ICP algorithm) can be adopted to determine the matching relationship between at least two groups of point cloud data unified to the target coordinate system, and then the matching relationship is based on the matching relationshipSplicing the point clouds according to the matching relation, thereby obtaining the complete point cloud P aiming at the wall to be detected _cloud = Σp (x, y, z), where P (x, y, z) represents single point cloud data. It is to be understood that the above-described point cloud registration algorithm is merely exemplary to facilitate understanding of the present disclosure, and any other point cloud registration algorithm may be used in the present disclosure, which is not limited in this disclosure.

According to the embodiment of the disclosure, the reference object is fixedly arranged relative to the wall to be detected, so that the point cloud data acquired under at least two acquisition parameters can be spliced conveniently, and the point cloud splicing efficiency and the accuracy of the point cloud splicing are improved.

Fig. 4 is a schematic diagram of determining wall point cloud data of a wall to be tested according to an embodiment of the disclosure.

According to the embodiment of the disclosure, in the case that the reference object fixedly arranged relative to the wall to be detected is arranged, the point cloud data of the reference object is correspondingly included in the point cloud data acquired by the acquisition equipment. When the point cloud data is projected, the point cloud data of the reference object needs to be removed from the point cloud data of the wall to be detected, which is obtained by splicing, so that the wall point cloud data which relatively only describes the wall to be detected is obtained. And then, based on the wall point cloud data, projecting each wall surface to a two-dimensional plane to obtain a two-dimensional image of each wall surface. Wherein, for example, each wall surface may be projected to a two-dimensional plane corresponding to each wall surface, and the corresponding two-dimensional plane may be, for example, any one plane in a normal direction of each wall surface, which is not limited in the present disclosure.

For example, the embodiment may perform the division processing on the point cloud data for the wall to be measured based on the relative position information between the reference object and the wall to be measured, and use the point cloud data obtained by the division processing as the wall point cloud data. Specifically, the process of performing segmentation processing on the point cloud data is a point cloud segmentation process, and the purpose of point cloud segmentation is to extract different objects in the point cloud data. In this embodiment, the segmentation threshold may be determined based on the relative position information, and the segmentation threshold may be set such that the point cloud data of the reference object is not within the threshold range, so that the point cloud segmentation may be performed based on the segmentation threshold, thereby obtaining the wall point cloud data.

In an embodiment, when the point cloud data is segmented, for example, the three-dimensional size of the wall to be measured may be considered in addition to the relative position information between the reference object and the wall to be measured. The three-dimensional size may be, for example, a target three-dimensional size determined based on a predetermined three-dimensional model of the wall to be measured. Based on the three-dimensional size and the relative position information of the target, a coordinate range of the wall to be detected in a coordinate system constructed for the reference object can be determined, and in the embodiment, point cloud segmentation can be performed by taking a boundary value of the coordinate range as a segmentation threshold. And eliminating other point cloud data except the point cloud data describing the wall to be tested, and eliminating interference of a reference object, the environment and the like in the scanning process of the scanner.

For example, as shown in fig. 4, in the embodiment 400, if the three-dimensional dimensions of the wall 410 to be measured are set to be length×width×height (l×w×h). And with respect to the vertex of the lower left corner of the wall to be measured, the origin O (0, 0) of the coordinate system constructed for the reference object 420 is located at a position with length l in the length direction, width w in the width direction, and height h in the height direction of the wall to be measured 410. Then, it can be determined according to the three-dimensional size of the wall 410 to be measured, and the coordinate range of the wall 410 to be measured in the coordinate system constructed for the reference object 420 is the range (L-L) of the X-axis direction, the range (W-W) of the Y-axis direction, and the range (H-H) of the Z-axis direction. The embodiment may use the three ranges as the three-axis segmentation threshold 401, reserve the point clouds located in the three ranges in the point cloud data of the wall 410 to be tested, and reject the point clouds not located in the three ranges, so as to obtain the wall point cloud data of the wall to be tested.

In an embodiment, point cloud data obtained by dividing point cloud data 402 for a wall to be measured based on a division threshold 401 may be used as divided point cloud data 403, and then wall point cloud data of the wall to be measured may be determined based on the divided point cloud data 403.

For example, the segmented point cloud data 403 may be subjected to outlier filtering, and the point cloud data obtained by the outlier filtering may be used as wall point cloud data of the wall to be measured. Through outlier filtering processing, noise points introduced by dust, winged insects and the like in the process of collecting point cloud data can be removed, and the accuracy of the determined wall point cloud data can be improved. It will be appreciated that the outlier filtering process is merely used as an example to facilitate understanding the disclosure, and the wall point cloud data may be obtained by performing filtering processes such as redundancy point removal and/or outlier removal on the segmented point cloud data, for example.

By way of example, the foregoing point cloud segmentation may also be used as a rough segmentation process, followed by fine segmentation of the segmented point cloud data 403, with the finely segmented point cloud data being used as wall point cloud data. In the fine segmentation, for example, the basis of fine segmentation can be determined based on a predetermined three-dimensional model of the wall to be detected, so that the finally determined masonry quality can reflect the difference between the real wall and the wall model, and the guide information can be provided for the masonry of the wall.

For example, before the post-segmentation point cloud data 403 is finely segmented, for example, the post-segmentation point cloud data 403 may be subjected to an outlier filtering process or the like, and then the post-filtering point cloud data obtained after the filtering process may be finely segmented.

For example, the segmented point cloud data 403 may be finely segmented according to a predetermined texture thickness of each of the walls to be measured. Wherein the predetermined texture thickness of each wall surface is determined based on the predetermined three-dimensional model of the wall to be measured described above.

Specifically, for example, the point cloud data 404 describing each wall surface in the segmented point cloud data 403 may be determined first, so as to obtain at least one set of point cloud data describing at least one wall surface included in the wall 410 to be tested, i.e. each set of point cloud data describes one wall surface. For example, if the reference object is set to be parallel to the outer wall surface of the wall 410 to be measured, and the Z axis of the coordinate system constructed for the reference object is parallel to the normal direction of the outer wall surface, the embodiment may divide the divided point cloud data into at least one group according to the coordinate values of the divided point cloud data in each coordinate axis direction in the coordinate system constructed for the reference object, so as to obtain at least one group of point cloud data describing at least one wall surface respectively. Or, a plane fitting algorithm may be used to perform plane fitting on the segmented point cloud data, so as to obtain at least one plane, and the point cloud data in the predetermined range of each plane obtained by fitting in the segmented point cloud data is divided into a group of point cloud data, so as to obtain at least one group of point cloud data.

After the point cloud data of each wall surface is obtained, a division threshold in the normal direction of each wall surface may be determined based on the predetermined texture thickness 405 of each wall surface and the point cloud data 404 describing each wall surface.

For example, the predetermined texture thickness 405 of each wall surface may be set to T according to the coordinate value of the point cloud data 404 describing the each wall surface along the normal direction of the each wall surface, taking the mode in the coordinate value as the center value Vc of the coordinate value of the each wall surface in the normal direction, then the embodiment may be described as [ V _c -(T+a)/2，V _c -(T+a)/2]As a division threshold value in the normal direction of the each wall surface. Wherein a is a super parameter, and the value of a can be set according to actual requirements. For example, a may be 0, or a may be any value greater than 0. By setting a larger than 0, the point cloud data describing each wall surface can be better reserved besides eliminating interference points through fine segmentation. This is because, under the condition that the actual texture thickness of the wall surface has a certain deviation from the predetermined texture thickness due to the masonry quality of the wall body to be measured, the method is based on [ V _c -T/2，V _c -T/2]Performing fine division results in the edge data in the point cloud data describing each wall surface being culled out.

For example, in determining the division threshold value in the normal direction of each wall surface, the position information 406 of each wall surface in the normal direction of each wall surface may be determined from the point cloud data 404 describing each wall surface. I.e., to determine coordinate values describing the point cloud data 404 of each wall surface along the normal direction of each wall surface. Then, a search range 407 in the normal direction of each wall surface may be determined from the position information 406 of each wall surface in the normal direction and the predetermined texture thickness 405 of each wall surface. For example, [ V ] described above _c -(T+a)/2，V _c -(T+a)/2]As a search range in the normal direction of the each wall surface. In an embodiment, the central value V of the coordinate value of each wall surface in the normal direction can be determined according to the relative position information between the reference object 420 and the wall 410 to be measured _c The present disclosure is not limited in this regard. After the search range is obtained, the point cloud data 404 describing each wall surface may be searched according to the search range, and the extreme point cloud data 408 in the normal direction of each wall surface in the point cloud data 404 describing each wall surface may be determined. For example, a point in the point cloud data 404 describing each wall surface at which the coordinate value along the normal direction of each wall surface is largest and a point in the point cloud data 408 describing each wall surface at which the coordinate value along the normal direction of each wall surface is smallest may be taken as the two extreme point cloud data. The embodiment may determine the division threshold 409 for each wall surface according to the coordinate values included in the extreme point cloud data 408 along the normal direction of each wall surface. According to the embodiment, the point cloud data are determined by searching the extreme point cloud data, so that the determined segmentation threshold can be more attached to the actual masonry effect of the wall to be tested, the point cloud data are segmented based on the segmentation threshold, the segmentation accuracy can be improved, and the reserved point cloud data are only the point cloud data describing the wall to be tested as much as possible. Furthermore, since the predetermined texture thickness is also considered in determining the segmentation threshold, the influence of the interfering object of a larger size on the segmentation accuracy can be avoided, and the accuracy of the determined segmentation threshold can be further improved.

After determining the division threshold value in the normal direction of each wall surface, the point cloud data may be finely divided according to the division threshold value in the normal direction of all the wall surfaces included in the wall 410 to be measured, for example, the post-division point cloud data 403 may be finely divided, or the foregoing post-filtering point cloud data may be finely divided, so as to obtain wall point cloud data 410' of the wall 410 to be measured.

Fig. 5 is a schematic diagram of two-dimensional projection of point cloud data according to an embodiment of the present disclosure.

According to the embodiments of the present disclosure, when each wall surface is projected onto a two-dimensional plane based on the point cloud data of the wall to be measured, for example, the principle described in the above-described embodiments may be adopted first, and the wall point cloud data of the wall to be measured may be determined based on the point cloud data of the wall to be measured. The wall point cloud data may be, for example, wall point cloud data 410' obtained in the embodiment described in fig. 4 above, which is not limited in this disclosure.

As shown in fig. 5, in an embodiment 500, the wall point cloud data of the wall to be measured is set to include coordinate values under a coordinate system 510 constructed for the reference object. In this embodiment 500, when two-dimensional projection is performed on each wall surface, for example, the determined wall point cloud data 501 of the wall to be measured may be transformed from the coordinate system 510 constructed for the reference object into the NDC coordinate system 520, so as to obtain the first point cloud data 502. Subsequently, the embodiment may project the first point cloud data to a two-dimensional plane corresponding to each wall surface, thereby obtaining a two-dimensional image 503 of each wall surface.

For example, before coordinate transformation is performed, the viewing space (e.g., coordinate system 510 constructed for the reference object) may be set to +X-axis to the right, +Y-axis to +Z-axis to a right-hand coordinate system outside the screen, with the viewing direction along the-Z-axis, i.e., looking into the screen. Through coordinate transformation, the points are transformed into regular observers (Canonical View Volume, abbreviated as CVV). Wherein the CVV is also referred to as homogeneous crop space, i.e., standardized device coordinate system 520. The CVV is a left-hand coordinate system with +X axis to the right, +Y axis to +Z axis pointing toward the inside of the screen. In the process of wall point cloud data P _e ＝(X _e ，Y _e ，Z _e ) Transforming the data into an NDC coordinate system to obtain first point cloud data P under the NDC coordinate system _n ＝(X _n ，Y _n ，Z _n ) The slave P can be accomplished by the projection matrix _e Point P to clipping space _c ＝(X _c ，Y _c ，Z _c ，W _c ) Is then transformed into P _c Perspective division can be performed to obtain P _n . By means of the transformation, the cuboid bounding box of the wall to be measured can be scaled into a normalized cuboid bounding box.

Wherein, for example, the projection matrix and the perspective can be dividedThe method is integrated into a perspective projection matrix, and P can be obtained through projection transformation and derivation _e And P _n The conversion relationship between them can be expressed by the following formulas (3) to (5), for example.

Wherein, the coordinate value of the X axis of the left side of a rectangular area cut by four side planes of a view object (frustum) on a near-sight cross section in a coordinate system 510 aiming at a reference object is m, the coordinate value of the X axis of the right side of the cut rectangular area in the coordinate system 510 aiming at the reference object is r, the coordinate value of the Y axis of the top side of the cut rectangular area in the coordinate system 510 aiming at the reference object is p, the coordinate value of the Y axis of the bottom side of the cut rectangular area in the coordinate system 510 aiming at the reference object is b, the coordinate value of the nearest distance from an observation point in the coordinate system 510 aiming at the reference object in the Z axis is-n, and the coordinate value of the farthest distance from the observation point in the coordinate system 510 aiming at the reference object in the Z axis is-f.

For example, after the first point cloud data 502 is obtained, the view port transformation may be performed on the first point cloud data 502 to obtain coordinate values of pixels corresponding to the two-dimensional image, so as to obtain the two-dimensional image 503 of each wall surface through conversion.

On the basis of this embodiment 500, in determining the actual depth information of each wall surface, for example, the second point cloud data corresponding to the target point cloud data involved in the projection of the two-dimensional image 503 of each wall surface in the first point cloud data may be determined first. Subsequently, the embodiment may determine the actual depth information of the wall to be measured according to the coordinate values included in the second point cloud data along the normal direction of each wall surface. For example, if the origin of the coordinate system constructed for the reference object is translated to the center position of the wall to be measured before perspective projection is performed, the embodiment may directly use the coordinate value included in the second point cloud data along the normal direction of each wall surface as depth information, otherwise, the origin of the NDC coordinate system needs to be translated according to the relative positional relationship between the origin of the coordinate system constructed for the reference object and the center position of the wall to be measured, and the coordinate value of the second point cloud data along the normal direction of each wall surface in the coordinate system after translation is used as depth information. It will be appreciated that the above-described method of determining depth information is merely exemplary to facilitate an understanding of the present disclosure, which is not limited thereto.

In this embodiment, by performing projection of the point cloud data based on the perspective projection matrix, accuracy and projection efficiency of the two-dimensional image obtained by final projection can be improved. Furthermore, the two-dimensional display of the difference between the actual depth information and the standard depth information is facilitated by projecting the point cloud to the two-dimensional plane and determining the depth information based on the two-dimensional plane.

Fig. 6 is a schematic illustration of the effect of embodying masonry quality according to an embodiment of the present disclosure.

In one embodiment, the masonry quality of the wall to be measured may be represented by a depth difference image representing pixel-by-pixel depth value differences.

In an embodiment 600, a depth difference image representing the masonry quality of a wall to be tested is shown in fig. 6, and in fig. 6, the larger the gray value is, the larger the depth value difference is.

Based on the method for determining the masonry quality provided by the disclosure, the disclosure also provides a device for determining the masonry quality. The device will be described in detail below in connection with fig. 7.

Fig. 7 is a block diagram of an apparatus for determining masonry quality according to an embodiment of the present disclosure.

As shown in fig. 7, the apparatus 700 for determining masonry quality of this embodiment may include a projection module 710, a depth determination module 720, and a masonry quality determination module 730.

The projection module 710 is configured to project, for each wall surface of at least one wall surface included in the wall to be measured, each wall surface onto a two-dimensional plane based on the point cloud data for the wall to be measured, and obtain a two-dimensional image of each wall surface. In an embodiment, the projection module 710 may be used to perform the operation S210 described above, which is not described herein.

The depth determining module 720 is configured to determine actual depth information of each wall surface based on target point cloud data in the point cloud data of the wall surface to be measured. The target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection. In an embodiment, the depth determining module 720 may be used to perform the operation S220 described above, which is not described herein.

The masonry quality determination module 730 is configured to determine masonry quality of a wall to be tested according to a difference between actual depth information of each wall surface and theoretical depth information of each wall surface. The theoretical depth information is determined based on a preset three-dimensional model of the wall to be measured. In an embodiment, the masonry quality determination module 730 may be used to perform operation S230 described above, for example, and will not be described in detail herein.

In an embodiment, the projection module 710 may include, for example, a wall point cloud determination sub-module, a point cloud transformation sub-module, and a projection sub-module. The wall point cloud determining submodule is used for determining wall point cloud data of the wall to be detected based on the point cloud data of the wall to be detected. The wall point cloud determining submodule is used for determining wall point cloud data of the wall to be detected based on the point cloud data of the wall to be detected. The point cloud transformation submodule is used for transforming the wall body point cloud data of the wall body to be measured into a standardized equipment coordinate system to obtain first point cloud data. The projection sub-module is used for projecting the first point cloud data to a two-dimensional plane corresponding to each wall surface to obtain a two-dimensional image of each wall surface. The depth determination module 720 may include, for example, a corresponding point cloud determination sub-module and a depth determination sub-module. The corresponding point cloud determining submodule is used for determining second point cloud data corresponding to the target point cloud data in the first point cloud data. The depth determination submodule is used for determining actual depth information of the wall to be measured according to coordinate values, included in the second point cloud data, along the normal direction of each wall.

In an embodiment, the apparatus 700 for determining masonry quality may further include a point cloud acquisition module, a point cloud transformation module, and a point cloud stitching module, for example. The point cloud acquisition module is used for acquiring original point cloud data acquired by the acquisition equipment aiming at the wall to be detected under at least two acquisition parameters. The point cloud transformation module is used for transforming the original point cloud data to the target coordinate system according to the transformation relation between the coordinate system constructed for the acquisition equipment and the target coordinate system, and obtaining transformed point cloud data. The point cloud splicing module is used for splicing the transformed point cloud data to obtain the point cloud data aiming at the wall to be detected. Wherein the target coordinate system is an arbitrary predetermined three-dimensional coordinate system.

In an embodiment, the original point cloud data includes point cloud data of a reference object fixedly arranged relative to the wall to be measured. The apparatus 700 for determining masonry quality may further include a transformation relation determining module for determining a transformation relation between a coordinate system constructed for the collection device and a target coordinate system constructed for the reference object according to a relative positional relation between the reference object and the collection device, for example.

In an embodiment, the point cloud data for the wall to be measured includes point cloud data of a reference object fixedly disposed with respect to the wall to be measured. The projection module 710 may include, for example, a wall point cloud determination sub-module and a projection sub-module. The wall point cloud determining submodule is used for determining wall point cloud data of the wall to be detected based on the point cloud data of the wall to be detected. The wall point cloud determining submodule may include, for example, a point cloud dividing unit and a wall point cloud determining unit. The point cloud segmentation unit is used for segmenting the point cloud data aiming at the wall to be detected based on the target three-dimensional size of the wall to be detected and the relative position information between the reference object and the wall to be detected, and obtaining segmented point cloud data. The wall point cloud determining unit is used for determining wall point cloud data of the wall to be detected based on the segmented point cloud data. The projection submodule is used for projecting each wall surface to a two-dimensional plane corresponding to each wall surface based on wall body point cloud data to obtain a two-dimensional image of each wall surface. The target three-dimensional size is determined based on a predetermined three-dimensional model of the wall to be measured.

In an embodiment, the wall point cloud determining unit may be specifically configured to determine wall point cloud data of the wall to be measured based on point cloud data obtained by performing outlier filtering processing on the segmented point cloud data.

In an embodiment, the wall point cloud determining unit may include a wall point cloud determining subunit, a segmentation threshold determining subunit, and a point cloud segmentation subunit, for example. The wall point cloud determining subunit is used for determining point cloud data describing each wall surface in the point cloud data after segmentation. The division threshold determining subunit is configured to determine a division threshold in a normal direction of each wall surface based on a predetermined texture thickness of each wall surface and point cloud data describing each wall surface. The point cloud segmentation subunit is used for carrying out segmentation processing on the point cloud data according to the segmentation threshold value in the normal direction of at least one wall surface based on the segmented point cloud data to obtain wall body point cloud data of the wall body to be detected. Wherein the predetermined texture thickness of each wall surface is determined based on a predetermined three-dimensional model.

According to an embodiment of the present disclosure, the above-mentioned segmentation threshold determination subunit is specifically configured to: determining the position information of each wall surface in the normal direction of each wall surface according to the point cloud data describing each wall surface; determining a search range in the normal direction of each wall surface according to the position information and the preset texture thickness of each wall surface; searching point cloud data describing each wall surface in a searching range, and determining extreme point cloud data in the normal direction of each wall surface; and determining a segmentation threshold value for each wall surface according to coordinate values included in the extreme point cloud data along the normal direction of each wall surface.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and applying personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated. In the technical scheme of the disclosure, the authorization or consent of the user is obtained before the personal information of the user is obtained or acquired.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 8 illustrates a schematic block diagram of an example electronic device 800 that may be used to implement the method of determining masonry quality of embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

Various components in device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as a method of determining masonry quality. For example, in some embodiments, the method of determining masonry quality may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM 802 and/or communication unit 809. When the computer program is loaded into RAM803 and executed by computing unit 801, one or more steps of the method of determining masonry quality described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the method of determining masonry quality by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS"). The server may also be a server of a distributed system or a server that incorporates a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A method of determining masonry quality, comprising:

for each wall surface of at least one wall surface included in a wall body to be measured, projecting the wall surface to a two-dimensional plane based on point cloud data of the wall body to be measured, and obtaining a two-dimensional image of the wall surface;

determining actual depth information of each wall surface based on target point cloud data in the point cloud data of the wall body to be detected; the target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection; and

Determining the masonry quality of the wall to be tested according to the difference between the actual depth information of each wall surface and the theoretical depth information of each wall surface,

the theoretical depth information is determined based on a preset three-dimensional model of the wall to be detected.

2. The method according to claim 1, wherein:

the projecting each wall surface to a two-dimensional plane based on the point cloud data of the wall body to be detected, and obtaining the two-dimensional image of each wall surface comprises:

determining wall point cloud data of the wall to be detected based on the point cloud data of the wall to be detected;

transforming the wall point cloud data of the wall to be detected into a standardized equipment coordinate system to obtain first point cloud data; and

projecting the first point cloud data to a two-dimensional plane corresponding to each wall surface to obtain a two-dimensional image of each wall surface;

the determining the actual depth information of each wall surface according to the target point cloud data in the point cloud data of the wall body to be detected comprises:

determining second point cloud data corresponding to the target point cloud data in the first point cloud data; and

and determining the actual depth information of the wall to be detected according to the coordinate values, included in the second point cloud data, along the normal direction of each wall surface.

3. The method of claim 1 or 2, further comprising:

acquiring original point cloud data acquired by acquisition equipment for the wall to be detected under at least two acquisition parameters;

transforming the original point cloud data to the target coordinate system according to the transformation relation between the coordinate system constructed for the acquisition equipment and the target coordinate system to obtain transformed point cloud data;

splicing the transformed point cloud data to obtain point cloud data aiming at the wall to be tested,

wherein the target coordinate system is an arbitrary predetermined three-dimensional coordinate system.

4. The method of claim 3, wherein the raw point cloud data comprises point cloud data of a reference fixedly disposed with respect to the wall to be measured; the method further comprises the steps of:

and determining a transformation relation between a coordinate system constructed for the acquisition equipment and the target coordinate system constructed for the reference object according to the relative position relation between the reference object and the acquisition equipment.

5. The method of any one of claims 1-4, wherein the point cloud data for the wall under test comprises point cloud data of a reference fixedly disposed with respect to the wall under test; projecting each wall surface to a two-dimensional plane based on the point cloud data of the wall to be detected, and obtaining the two-dimensional image of each wall surface comprises:

Determining wall point cloud data of the wall to be measured based on the point cloud data for the wall to be measured by:

dividing point cloud data aiming at the wall to be detected based on the target three-dimensional size of the wall to be detected and relative position information between the reference object and the wall to be detected to obtain divided point cloud data; and

determining wall point cloud data of the wall to be detected based on the segmented point cloud data; and

projecting each wall surface to a two-dimensional plane corresponding to each wall surface based on the wall point cloud data to obtain a two-dimensional image of each wall surface,

wherein the target three-dimensional size is determined based on a predetermined three-dimensional model of the wall to be measured.

6. The method of claim 5, wherein the determining wall point cloud data for the wall under test based on the segmented point cloud data comprises:

and determining the wall body point cloud data of the wall body to be detected based on the point cloud data obtained by performing outlier filtering processing on the segmented point cloud data.

7. The method of claim 5 or 6, wherein the determining wall point cloud data of the wall under test based on the segmented point cloud data comprises:

Determining point cloud data describing each wall surface in the partitioned point cloud data;

determining a segmentation threshold value in the normal direction of each wall surface based on the preset texture thickness of each wall surface and point cloud data describing each wall surface; and

based on the point cloud data after segmentation, carrying out segmentation processing on the point cloud data according to a segmentation threshold value in the normal direction of the at least one wall surface to obtain wall point cloud data of the wall to be detected,

wherein the predetermined texture thickness of each wall surface is determined based on the predetermined three-dimensional model.

8. The method of claim 7, wherein the determining a segmentation threshold in a normal direction of each wall surface based on the predetermined texture thickness of each wall surface and point cloud data describing each wall surface comprises:

determining the position information of each wall surface in the normal direction of each wall surface according to the point cloud data describing each wall surface;

determining a search range in the normal direction of each wall surface according to the position information and the preset texture thickness of each wall surface;

searching point cloud data describing each wall surface in the searching range, and determining extreme point cloud data in the normal direction of each wall surface; and

And determining a segmentation threshold value for each wall surface according to coordinate values, included in the extreme point cloud data, along the normal direction of each wall surface.

9. An apparatus for determining masonry quality, comprising:

the projection module is used for projecting each wall surface to a two-dimensional plane based on point cloud data aiming at the wall surface to be detected, so as to obtain a two-dimensional image of each wall surface;

the depth determining module is used for determining the actual depth information of each wall surface based on target point cloud data in the point cloud data of the wall body to be detected; the target point cloud data are the point cloud data involved in the process of obtaining the two-dimensional image of each wall surface through projection; and

a masonry quality determining module, configured to determine masonry quality of the wall to be tested according to a difference between actual depth information of each wall surface and theoretical depth information of each wall surface,

10. The apparatus of claim 9, wherein:

the projection module includes:

the wall point cloud determining submodule is used for determining wall point cloud data of the wall to be detected based on the point cloud data of the wall to be detected;

The point cloud transformation submodule is used for transforming the wall point cloud data of the wall to be measured into a standardized equipment coordinate system to obtain first point cloud data; and

the projection submodule is used for projecting the first point cloud data to a two-dimensional plane corresponding to each wall surface to obtain a two-dimensional image of each wall surface;

the depth determination module includes:

a corresponding point cloud determining sub-module, configured to determine second point cloud data corresponding to the target point cloud data in the first point cloud data; and

and the depth determination submodule is used for determining the actual depth information of the wall to be detected according to the coordinate value included in the second point cloud data along the normal direction of each wall surface.

11. The apparatus of claim 9 or 10, further comprising:

the point cloud acquisition module is used for acquiring original point cloud data acquired by the acquisition equipment aiming at the wall to be detected under at least two acquisition parameters;

the point cloud transformation module is used for transforming the original point cloud data to the target coordinate system according to the transformation relation between the coordinate system constructed for the acquisition equipment and the target coordinate system to obtain transformed point cloud data; and

A point cloud splicing module for splicing the transformed point cloud data to obtain the point cloud data aiming at the wall to be tested,

12. The apparatus of claim 11, wherein the raw point cloud data comprises point cloud data of a reference fixedly disposed with respect to the wall to be measured; the apparatus further comprises:

and the transformation relation determining module is used for determining the transformation relation between the coordinate system constructed for the acquisition equipment and the target coordinate system constructed for the reference object according to the relative position relation between the reference object and the acquisition equipment.

13. The apparatus of any one of claims 9-12, wherein the point cloud data for the wall under test comprises point cloud data of a reference fixedly disposed with respect to the wall under test; the projection module includes:

the wall point cloud determining submodule is used for determining wall point cloud data of the wall to be detected based on the point cloud data of the wall to be detected; the wall point cloud determining submodule comprises:

the point cloud segmentation unit is used for segmenting the point cloud data aiming at the wall to be detected based on the target three-dimensional size of the wall to be detected and the relative position information between the reference object and the wall to be detected to obtain segmented point cloud data; and

The wall point cloud determining unit is used for determining wall point cloud data of the wall to be detected based on the segmented point cloud data;

a projection sub-module for projecting each wall surface to a two-dimensional plane corresponding to each wall surface based on the wall point cloud data to obtain a two-dimensional image of each wall surface,

14. The apparatus of claim 13, wherein the wall point cloud determining unit is configured to:

15. The apparatus of claim 13 or 14, wherein the wall point cloud determining unit comprises:

a wall point cloud determining subunit, configured to determine point cloud data describing each wall surface in the segmented point cloud data;

a division threshold determining subunit configured to determine a division threshold in a normal direction of each wall surface based on a predetermined texture thickness of the each wall surface and point cloud data describing the each wall surface; and

a point cloud segmentation subunit, configured to segment the point cloud data according to a segmentation threshold value in a normal direction of the at least one wall surface based on the segmented point cloud data, to obtain wall point cloud data of the wall to be tested,

16. The apparatus of claim 15, wherein the segmentation threshold determination subunit is to:

17. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 8.

18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.

19. A computer program product comprising computer programs/instructions stored on at least one of a readable storage medium and an electronic device, which when executed by a processor, implement the steps of the method according to any one of claims 1 to 8.