CN113496461B - Processing method and device of point cloud data, computer equipment and storage medium - Google Patents

Processing method and device of point cloud data, computer equipment and storage medium Download PDF

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CN113496461B
CN113496461B CN202010190846.1A CN202010190846A CN113496461B CN 113496461 B CN113496461 B CN 113496461B CN 202010190846 A CN202010190846 A CN 202010190846A CN 113496461 B CN113496461 B CN 113496461B
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point cloud
cloud data
dsm
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partition
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CN113496461A (en
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池鹏可
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Guangzhou Xaircraft Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows

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Abstract

The embodiment of the invention discloses a point cloud data processing method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data; calculating the number of unilateral blocks matched with the point cloud data according to the total coverage, the ideal unilateral size of the single DSM and the DSM resolution; dividing the point cloud data into a plurality of point cloud blocks according to the total coverage, the single-side block number and the pre-overlapping degree; and calculating the partition DSM corresponding to each point cloud partition, and splicing the partition DSMs to obtain the complete DSM corresponding to the point cloud data. The technical scheme of the embodiment of the invention realizes the high-speed and effective generation of DSM under the premise of limited computer running memory.

Description

Processing method and device of point cloud data, computer equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of data image processing, in particular to a method and a device for processing point cloud data, computer equipment and a storage medium.
Background
With the continuous development of unmanned aerial vehicle shooting technology, by means of aerial unmanned aerial vehicle, ground image map can be acquired rapidly, full-automatic three-dimensional modeling is achieved, and results such as DSM (Digital Surface Model ), DOM (Digital Orthophoto Map, digital forward image map) and the like are output.
In the prior art, an image processing device needs to firstly acquire a ground image map under a set scene acquired by an aerial unmanned aerial vehicle, generate point cloud data corresponding to the ground image map, then perform interpolation operation on the point cloud data to obtain a corresponding DSM, and finally perform orthographic correction and color balance adjustment on the DSM to obtain a matched DOM.
The inventors have found that the following drawbacks exist in the prior art in the process of implementing the present invention: when the data range and the data volume of the three-dimensional point cloud are too large and the computer is limited in operation, the interpolation operation cannot be utilized to perform interpolation calculation on the whole three-dimensional point cloud at one time to obtain the whole DSM, namely the corresponding DOM cannot be generated through one-time calculation.
Disclosure of Invention
The embodiment of the invention provides a processing method, a processing device, computer equipment and a storage medium for point cloud data, which realize high-speed and effective generation of DSM (digital multimedia subsystem) on the premise of limited computer running memory.
In a first aspect, an embodiment of the present invention provides a method for processing point cloud data, including:
calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data;
Calculating the number of unilateral blocks matched with the point cloud data according to the total coverage, the ideal unilateral size of a single DSM and the DSM resolution;
Dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree;
And calculating the partition DSM corresponding to each point cloud partition, and splicing the partition DSM to obtain the complete DSM corresponding to the point cloud data.
In a second aspect, an embodiment of the present invention further provides a device for processing point cloud data, including:
The coverage calculating module is used for calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data;
The block number calculation module is used for calculating the single-side block number matched with the point cloud data according to the overall coverage, the ideal single-side size of the single DSM and the DSM resolution;
The point cloud data dividing module is used for dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree;
The DSM generation module is used for calculating the partitioned DSMs corresponding to the point cloud partitioned blocks respectively, and splicing the partitioned DSMs to obtain the complete DSMs corresponding to the point cloud data.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, including:
one or more processors;
a storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for processing point cloud data provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored, where the program when executed by a processor implements the method for processing point cloud data provided in any embodiment of the present invention.
According to the technical scheme of obtaining the complete DSM corresponding to the point cloud data by calculating the number of single-side blocks matched with the point cloud data according to the total coverage area of the point cloud data, the ideal single-side size of a single DSM and the resolution ratio of the DSM, dividing the point cloud data into a plurality of point cloud blocks according to the preset overlapping degree, calculating the blocks DSM corresponding to the point cloud blocks respectively, and splicing the blocks DSM, aiming at the technical problem that the prior art cannot obtain the complete DSM by carrying out interpolation calculation on the whole three-dimensional point cloud at one time by utilizing interpolation operation, a new point cloud data block dividing mode is provided, and the DSM is generated at high speed and effectively on the premise of limited computer operation memory.
Drawings
Fig. 1 is a flow chart of a method for processing point cloud data according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for processing point cloud data according to a second embodiment of the present invention;
fig. 3a is a schematic flow chart of a method for processing point cloud data according to a third embodiment of the present invention;
FIG. 3b is a schematic view of DSM calculated from the overlapping degree of unused point cloud segments;
FIG. 3c is a schematic view of a DSM calculated using the degree of overlap of point cloud partitions;
FIG. 3d is a schematic view of the resulting DOM calculated without the use of DSM for overlap;
FIG. 3e is a schematic view of the resulting DOM calculated using the overlap of the DSM;
fig. 4 is a schematic structural diagram of a processing device for point cloud data according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof.
Example 1
Fig. 1 is a flow chart of a processing method of point cloud data according to an embodiment of the present invention. The method of the embodiment of the invention is suitable for the situation that after the corresponding partitioned DSM is generated for the point cloud data with large data volume in a partitioned manner, the complete DSM is synthesized, and the method of the embodiment of the invention can be executed by the processing device of the point cloud data, which can be realized in a software and/or hardware manner and can be generally integrated in a server or terminal equipment with a data processing function. As shown in fig. 1, the method may include:
s110, calculating the overall coverage of the point cloud data according to the horizontal coordinates of the point cloud data points in the point cloud data.
Wherein, the point cloud data is formed by a plurality of point cloud data points, and each point cloud data point contains three-dimensional coordinates, namely: the horizontal coordinates formed by the coordinate values on the X-axis and the coordinate values on the Y-axis, and the depth information may contain color information, reflection intensity information, or the like. The overall coverage of the point cloud data is determined by the horizontal coordinate range of the individual point cloud data points. Generally, the larger the horizontal coordinate range covered by the point cloud data points, the larger the data volume of the point cloud data, and therefore, the overall coverage of the point cloud data can be used as one standard of the point cloud partitioning.
The point cloud data can be generated according to a ground image map under a set scene acquired by the aerial unmanned aerial vehicle and serve as basic data for subsequent generation of DSM and DOM.
In general, the DSM and DOM are both regular rectangular shapes. Therefore, when the overall coverage of the point cloud data is determined, the maximum circumscribed rectangle within the coverage of the point cloud data can be calculated as the overall coverage of the point cloud data. Specifically, the minimum value min_x of the transverse range (X axis) and the maximum value max_x of the transverse range, the minimum value min_y of the longitudinal range (Y axis) and the maximum value max_y of the longitudinal range may be obtained by traversing in each data point of the point cloud data, and a rectangle surrounded by the four extreme points may be used as the overall coverage of the point cloud data.
And S120, calculating the single-side block number matched with the point cloud data according to the overall coverage, the ideal single-side size of the single DSM and the DSM resolution.
In this embodiment, a new partitioning method is provided to solve the problem that in the prior art, complete DSM cannot be directly generated for point cloud data with a large data volume, first, the point cloud data is partitioned into a plurality of point cloud partitions, then, partitioning DSMs corresponding to each point cloud partition are generated, and then, each partitioning DSM is combined to obtain the final complete DSM.
Wherein, the ideal single-side size of a single block DSM can be determined firstly according to the capacity of the computer memory, and the ideal single-side size is determined based on the maximum size of a single-time generatable block DSM of the computer memory.
The ideal single-side size specifically refers to the number of pixels included in the DSM in the length direction or the number of pixels included in the width direction. Alternatively, since the shape of the DSM is generally rectangular, the maximum value of the longest side (length or width) of the monolithic DSM may be calculated as the ideal single-side size of the monolithic DSM for the convenience of subsequent calculations. Accordingly, as long as each side of the partitioned DSM obtained by final partitioning does not exceed the ideal single-side size of the single DSM, the requirement on the memory of the computer can be met.
The DSM resolution is the pixel size expressed by the unit of ground distance, which may be meters per pixel, centimeters per pixel, or the like. Therefore, by calculating the product of the resolution of the DSM and the ideal single-side size of the monolithic DSM, the longest ground distance of the monolithic DSM in the X-axis direction or the Y-axis direction can be obtained, and thus the maximum ground coverage of the monolithic DSM can be obtained, that is: square of ideal unilateral size for monolithic DSM.
The DSM resolution may be a preset empirical value, or a GSD (GroundSample Distance, ground sampling distance) of each ground image for forming the point cloud data may be first acquired, and the DSM resolution may be determined based on the GSD, which is not limited by the embodiment of the present invention. Typically, the GSD may be taken directly as DSM resolution.
In this embodiment, the inventor has further simplified the partitioning operation, and can set a uniform single-side partitioning number for the length and width of the overall coverage, where the single-side partitioning number refers to the number of portions divided on the rectangular side in the X-axis direction in the overall coverage and the number of portions divided on the rectangular side in the Y-axis direction in the overall coverage, and the two portions are the same.
In this embodiment, the total number of pieces of the single DSM included in the overall coverage can be calculated simply from the overall coverage, and the maximum coverage of the single DSM. The number of single-sided segments that the point cloud data matches is then calculated according to some empirical algorithm, such as an open square process, or other manner of processing.
S130, dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree.
In this embodiment, according to the overall coverage area and the single-side block number, the coordinate ranges of each point cloud block on the X-axis and the Y-axis may be calculated, and then, according to the horizontal coordinate values (the coordinate values on the X-axis and the coordinate values on the Y-axis) of each point cloud data point in the point cloud data, each point cloud data point in the point cloud data may be divided into corresponding point cloud blocks, so as to implement the block processing of the point cloud data.
In this embodiment, in order to ensure that the resulting complete DSM has no obvious splice marks at the edges of the individual tiles DSM, i.e.: the crack artifact needs to have a certain overlapping degree among the point cloud blocks, so that the coordinate range of each point cloud data can be outwards expanded according to the X-axis overlapping degree and the Y-axis overlapping degree of the set values or the uniform overlapping degree, so that a certain overlapping area is formed among the adjacent point cloud blocks.
For example, the coordinate range of one point cloud block a on the X-axis is [ tile_min_x, tile_max_x ], the coordinate range on the Y-axis is [ tile_min_y, tile_max_y ], the set uniform overlapping degree is overlapp _step, the point cloud data a is expanded by the overlapping degree, the new X-axis coordinate range of the point cloud data a is obtained and expanded to [ tile_min_x-overlapp _step, tile_max_x+ overlapp _step ], and the new Y-axis coordinate range of the point cloud data a is obtained and expanded to [ tile_min_y-overlapp _step, tile_max_y+ overlapp _step ].
The overlapping degree may be a preset empirical value, or may be calculated according to the density of the point cloud data (the number of the point cloud data points included in the unit area), which is not limited in this embodiment.
S140, calculating the partition DSM corresponding to each point cloud partition, and splicing the partition DSMs to obtain the complete DSM corresponding to the point cloud data.
In this embodiment, interpolation operation may be performed on each point cloud partition after the overlapping degree expansion, after a corresponding partition DSM is obtained, each partition DSM may be intercepted according to the coverage area of each point cloud partition before the overlapping degree expansion, so as to ensure that there is no overlapping area between each partition DSM, and further, each intercepted partition DSM may be simply spliced to obtain a complete DSM.
Or performing interpolation operation on each point cloud partition subjected to overlap degree expansion to obtain corresponding partition DSMs, directly splicing the corresponding partition DSMs with the overlap region, and processing the overlap region by using a certain overlap processing algorithm to obtain the complete DSMs.
According to the technical scheme of obtaining the complete DSM corresponding to the point cloud data by calculating the number of single-side blocks matched with the point cloud data according to the total coverage area of the point cloud data, the ideal single-side size of a single DSM and the resolution ratio of the DSM, dividing the point cloud data into a plurality of point cloud blocks according to the preset overlapping degree, calculating the blocks DSM corresponding to the point cloud blocks respectively, and splicing the blocks DSM, aiming at the technical problem that the prior art cannot obtain the complete DSM by carrying out interpolation calculation on the whole three-dimensional point cloud at one time by utilizing interpolation operation, a novel point cloud data block dividing mode is provided, and the DSM is generated at high speed and effectively on the premise of limited computer operation memory.
Example two
Fig. 2 is a flow chart of a processing method of point cloud data according to a second embodiment of the present invention, in this embodiment, a manner of dividing the point cloud data into a plurality of block point clouds is further defined, and as shown in fig. 2, the method may include:
and S210, calculating the overall coverage of the point cloud data according to the horizontal coordinates of the point cloud data points in the point cloud data.
Optionally, after traversing each point cloud data point of the point cloud data to obtain a minimum value of a transverse range of the point cloud data as min_x and a maximum value of the transverse range as max_x, and a minimum value of a longitudinal range as min_y and a maximum value of the longitudinal range as max_y, taking the difference value of max_x-min_x as the horizontal length in the overall coverage area and taking the difference value of max_y-min_y as the horizontal width in the overall coverage area. Taking the product of (max_x-min_x) × (max_y-min_y) as the total coverage.
S220, calculating the single-side block number matched with the point cloud data according to the overall coverage, the ideal single-side size of the single DSM and the DSM resolution.
The inventor finds that the single-side block number obtained by calculation through the following formula has the best block effect on the basis of ensuring that the same single-side block number is separated in the X-axis direction and the Y-axis direction, and the DSM effect obtained by final calculation is also the best.
Specifically, according to the formula: and calculating a unilateral block number spilt _tiles matched with the point cloud data.
Wherein S is the total coverage, S is the product of the horizontal width and the horizontal length of the point cloud data, tile_max_size is the ideal single-side size of a single DSM, and resolution is the DSM resolution.
In this embodiment, a GSD of a ground image map that generates point cloud data may be calculated, and the GSD may be used as the DSM resolution.
Specifically, a GSD may be calculated according to an image acquisition parameter matched with the point cloud data, and the DSM resolution may be determined according to the GSD.
In one specific example, the formula may be according to:
Gsd= [ FLIGHTHEIGHT x SensorWidth ]/[ FocalLength x IMAGEWIDTH ], GSD is calculated.
Or may be according to the formula:
Gsd= [ FLIGHTHEIGHT x SensorHeight ]/[ FocalLength x IMAGEHEIGHT ], GSD is calculated.
Wherein FLIGHTHEIGHT is the flying height of the unmanned aerial vehicle when shooting the ground image, sensorWidth is the resolution width of the camera shooting the ground image, sensorHeight is the resolution height of the camera shooting the ground image, focalLength is the focal length of the camera shooting the ground image, IMAGEWIDTH is the photo width of the ground image, IMAGEHEIGHT is the photo height of the ground image.
And S230, calculating a first horizontal coordinate range corresponding to each point cloud partition according to the overall coverage range and the single-side partition number.
Wherein the first horizontal coordinate range includes: an X-axis coordinate range for each point cloud tile, and a Y-axis coordinate range for each point cloud tile.
Specifically, if the total coverage of the point cloud data is assumed to be the product of the horizontal width boundary_x and the horizontal length boundary_y, the single-side block number split_tiles is the number of equal parts for the horizontal width and the horizontal length. Therefore, the width value tile_x=boundary_x/split_tiles of each block of point cloud data, and the length value tile_y=boundary_y/split_tiles of each block of point cloud data.
As described above, after the four extreme points of the above-described point cloud data, that is, after the edge point positions of the point cloud data have been known, the first horizontal coordinate ranges respectively corresponding to each point cloud segment may be calculated respectively.
In a specific example, the minimum value of the lateral range of the point cloud data is min_x, the maximum value of the lateral range is max_x, the minimum value of the longitudinal range is min_y, the maximum value of the longitudinal range is max_y, the single-side block number is split_tiles, and the split_tiles are 2, and then 4 point cloud blocks, for example, a point cloud block a, a point cloud block B, a point cloud block C and a point cloud block D, may be determined according to the point cloud data.
The coordinate ranges of the 4 point cloud blocks are respectively as follows: the X-axis coordinate range of the point cloud block A is [ min_x, min_x+tile_x ], and the Y-axis coordinate range is [ min_y, min_y+tile_y ]; the X-axis coordinate range of the point cloud block B is [ min_x+tile_x, max_x ], and the Y-axis coordinate range is [ min_y, min_y+tile_y ]; the X-axis coordinate range of the point cloud block C is [ min_x, min_x+tile_x ], and the Y-axis coordinate range is [ min_y+tile_y, max_y ]; the X-axis coordinate range of the point cloud partition D is [ min_x+tile_x, max_x ], and the Y-axis coordinate range is [ min_y+tile_y, max_y ].
S240, updating each first horizontal coordinate range into a second horizontal coordinate range according to the preset overlapping degree.
As described above, after determining the overlapping degree, the overlapping degree may be used to extend the first horizontal coordinate range of each point cloud partition, so as to obtain a corresponding second horizontal coordinate range, so as to ensure that different partition point clouds have a certain overlapping area.
In this embodiment, the overlapping degree of the point cloud data may be calculated according to the density of the point cloud data.
Optionally, the thickness of the point cloud data may be calculated according to the total data amount of the point cloud data points included in the point cloud data and the total coverage area; and further calculating the overlapping degree according to the density.
Specifically, the formula may be: pointcloud _density=points_number/S, and calculating the density pointcloud _density of the point cloud data, where points_number is the total data amount of the point cloud data points included in the point cloud data, and S is the total coverage.
Meanwhile, according to the formula: calculating the overlapping degree overlapp _step; pointcloud _density is the consistency, A is a preset empirical constant. Typically, a may be 10.
In the previous example, in the first horizontal coordinate range of the point cloud partition a: the X-axis coordinate range is [ min_x, min_x+tile_x ], the Y-axis coordinate range is [ min_y, min_y+tile_y ], and after the overlapping degree is expanded, the second horizontal coordinate range of the point cloud partition A is as follows: the X-axis coordinate range is [ min_x-overlapp _step, min_x+tile_x+ overlapp _step ], and the Y-axis coordinate range is [ min_y-overlapp _step, min_x+tile_y+ overlapp _step ].
S250, distributing each point cloud data point in the point cloud data to a corresponding partitioned point cloud according to a second horizontal coordinate range corresponding to each point cloud partition.
In this embodiment, after obtaining the second horizontal coordinate ranges respectively corresponding to each point cloud partition, each point cloud data point may be divided into a plurality of matched partition point clouds according to the horizontal coordinate range of each point cloud data point in the point cloud data, so as to divide the point cloud data into a plurality of partition point clouds.
S260, calculating the partition DSM corresponding to each point cloud partition, and splicing the partition DSMs to obtain the complete DSM corresponding to the point cloud data.
According to the technical scheme, the single-side block number matched with the point cloud data and the overlapping degree of each block point cloud are planned and set in a reasonable planning mode, a simple and effective block mode of the point cloud data is provided, and DSM is generated efficiently and rapidly on the premise of limited computer running memory.
Example III
Fig. 3a is a flow chart of a processing method of point cloud data according to a third embodiment of the present invention, in this embodiment, a manner of calculating a complete DSM and DOM according to each point cloud partition is further defined, and as shown in fig. 3a, the method may include:
s310, calculating the overall coverage of the point cloud data according to the horizontal coordinates of the point cloud data points in the point cloud data.
S320, calculating the single-side block number matched with the point cloud data according to the overall coverage, the ideal single-side size of the single DSM and the DSM resolution.
S330, dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree.
And S340, carrying out interpolation processing on the point cloud data points in each point cloud partition by adopting a preset interpolation algorithm.
In this embodiment, interpolation processing may be performed on the point cloud data points in each point cloud partition according to the second horizontal coordinate range, the DSM resolution, and the preset interpolation algorithm corresponding to each point cloud partition.
The interpolation algorithm (Inverse-DISTANCE WEIGHTED, IDW), the nearest neighbor interpolation algorithm (nearest-neighbor), or the deluxe triangulation interpolation algorithm (delaunaytriangular) may be used to interpolate the point cloud data points in each point cloud partition, so as to obtain a partition DSM that meets the requirement.
And S350, generating a block DSM corresponding to each first horizontal coordinate range according to the point cloud data points and the interpolation points falling in each first horizontal coordinate range.
In this embodiment, since the block DSM is obtained by interpolation processing using the respective point cloud data in the second horizontal coordinate range, each DSM in the interpolated first horizontal coordinate range has eliminated the crack artifact. Accordingly, the split DSM in the second horizontal coordinate range does not need to be spliced, because the above-mentioned splicing method needs to perform a certain overlapping area processing. Therefore, in this embodiment, the split DSM corresponding to each of the first horizontal coordinate ranges is directly acquired to perform the stitching process, so as to simplify the computational complexity of the subsequent stitching process.
And S360, splicing the partitioned DSMs to obtain a complete DSM corresponding to the point cloud data.
In this embodiment, a resampling mosaic splicing manner may be adopted to splice the partitioned DSMs to obtain a complete DSM corresponding to the point cloud data.
S370, re-blocking the complete DSM by using each second horizontal coordinate range to obtain a plurality of new blocked DSMs.
In this embodiment, because the complete DOM cannot be obtained at one time, after the corresponding partitioned DOM is obtained for each partitioned DSM, the complete DOM is obtained by splicing the partitioned DOMs. In order to ensure that the DOM obtained by final splicing can also eliminate crack artifacts, a certain degree of overlap between the blocks DSM needs to be ensured. Therefore, after the complete DSM is obtained, the complete DSM can be directly re-segmented based on a second horizontal coordinate range with a certain degree of overlap, so that a plurality of new segmented DSMs are obtained to meet the requirement of the degree of overlap.
S380, carrying out orthorectification and color balance adjustment on each new partitioned DSM according to the matched aerial image, and calculating partitioned DOMs corresponding to each new partitioned DSM.
S390, nesting, fusing and splicing each partitioned DOM to obtain a complete DOM corresponding to the point cloud data.
Wherein, a DSM schematic diagram obtained by calculating the overlapping degree of the unused point cloud segments is shown in fig. 3 b; FIG. 3c shows a DSM schematic calculated using the degree of overlap of the point cloud partitions; the resulting DOM schematic of the overlap computation without DSM is shown in FIG. 3 d; the resulting DOM schematic calculated using the overlap of the DSM is shown in FIG. 3 e. As can be seen from the above figures, the split artifacts in the complete DSM can be significantly eliminated by splicing the segmented DSM obtained by using the point cloud segmentation calculation with overlapping degree, and the split artifacts in the complete DOM can be significantly eliminated by splicing the segmented DOM obtained by using the DSM calculation with overlapping degree.
According to the technical scheme, the complete DSM and the complete DOM are obtained through setting the point cloud partition with the set overlapping degree and calculating the partitioned DSM, so that the crack artifacts in the complete DSM and the complete DOM obtained through the partitioning processing operation can be eliminated, and the display effect of the complete DSM and the complete DOM is improved.
Example IV
Fig. 4 is a schematic structural diagram of a processing device for point cloud data according to a fourth embodiment of the present invention, where, as shown in fig. 4, the device includes: coverage calculation module 410, block count calculation module 420, point cloud data partitioning module 430, and DSM generation module 440. Wherein:
The coverage calculating module 410 is configured to calculate an overall coverage of the point cloud data according to horizontal coordinates of each point cloud data point in the point cloud data.
The block number calculation module 420 is configured to calculate a single-sided block number matched with the point cloud data according to the overall coverage, an ideal single-sided size of the single-sided DSM, and the DSM resolution.
The point cloud data dividing module 430 is configured to divide the point cloud data into a plurality of point cloud segments according to the overall coverage, the single-side segment number, and a preset overlapping degree.
The DSM generating module 440 is configured to calculate a partition DSM corresponding to each of the point cloud partitions, and splice each of the partition DSMs to obtain a complete DSM corresponding to the point cloud data.
According to the technical scheme of obtaining the complete DSM corresponding to the point cloud data by calculating the number of single-side blocks matched with the point cloud data according to the total coverage area of the point cloud data, the ideal single-side size of a single DSM and the resolution ratio of the DSM, dividing the point cloud data into a plurality of point cloud blocks according to the preset overlapping degree, calculating the blocks DSM corresponding to the point cloud blocks respectively, and splicing the blocks DSM, aiming at the technical problem that the prior art cannot obtain the complete DSM by carrying out interpolation calculation on the whole three-dimensional point cloud at one time by utilizing interpolation operation, a novel point cloud data block dividing mode is provided, and the DSM is generated at high speed and effectively on the premise of limited computer operation memory.
Based on the above embodiments, the point cloud data dividing module 430 may specifically be configured to:
According to the overall coverage range and the unilateral block number, calculating a first horizontal coordinate range corresponding to each point cloud block;
Updating each first horizontal coordinate range into a second horizontal coordinate range according to the preset overlapping degree;
And distributing each point cloud data point in the point cloud data to a corresponding partitioned point cloud according to a second horizontal coordinate range respectively corresponding to each point cloud partition.
On the basis of the above embodiments, the apparatus may further include a DSM resolution determination module for:
Before calculating a single-sided block number matched with the point cloud data according to the overall coverage, an ideal single-sided size of a single DSM and a DSM resolution, calculating a GSD according to image acquisition parameters matched with the point cloud data, and determining the DSM resolution according to the GSD.
On the basis of the above embodiments, the method may further include:
The density calculating unit is used for calculating the density of the point cloud data according to the total data amount of the point cloud data points included in the point cloud data and the total coverage area before the point cloud data are divided into a plurality of point cloud blocks according to the total coverage area, the single-side block number and the preset overlapping degree;
and the overlapping degree calculating unit is used for calculating the overlapping degree according to the density.
Based on the above embodiments, the block number calculation module 420 may be specifically configured to:
according to the formula: Calculating a unilateral block number spilt _tiles matched with the point cloud data;
Wherein S is the total coverage, S is the product of the horizontal width and the horizontal length of the point cloud data, tile_max_size is the ideal single-side size of a single DSM, and resolution is the DSM resolution.
On the basis of the above embodiments, the overlapping degree calculating unit may be specifically configured to:
according to the formula: Calculating the overlapping degree overlapp _step; pointcloud _density is the consistency, A is a preset empirical constant.
Based on the above embodiments, the block number calculation module 420 may be specifically configured to:
performing interpolation processing on the point cloud data points in each point cloud partition by adopting a preset interpolation algorithm;
And generating a partitioned DSM corresponding to each first horizontal coordinate range according to the point cloud data points and the interpolation points falling in each first horizontal coordinate range.
On the basis of the above embodiments, the system may further include a DOM generating module, configured to: after obtaining the complete DSM corresponding to the point cloud data, re-blocking the complete DSM by using each second horizontal coordinate range to obtain a plurality of new blocked DSMs;
Carrying out orthorectification and color balance adjustment on each new partitioned DSM according to the matched aerial image, and calculating partitioned DOMs corresponding to each new partitioned DSM respectively;
And nesting, fusing and splicing each block DOM to obtain a complete DOM corresponding to the point cloud data.
The processing device for the point cloud data provided by the embodiment of the invention can execute the processing method for the point cloud data provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention. Fig. 5 illustrates a block diagram of a computer device 412 suitable for use in implementing embodiments of the present invention. The computer device 412 shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in FIG. 5, computer device 412 is in the form of a general purpose computing device. Components of computer device 412 may include, but are not limited to: one or more processors 416, a storage 428, and a bus 418 that connects the various system components (including the storage 428 and the processors 416).
Bus 418 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry standard architecture (Industry Standard Architecture, ISA) bus, micro channel architecture (Micro Channel Architecture, MCA) bus, enhanced ISA bus, video electronics standards association (Video Electronics Standards Association, VESA) local bus, and peripheral component interconnect (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus.
Computer device 412 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 412 and includes both volatile and nonvolatile media, removable and non-removable media.
The storage 428 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 430 and/or cache memory 432. The computer device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a disk drive for reading from and writing to a removable nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable nonvolatile optical disk (e.g., a Compact Disc-Read Only Memory (CD-ROM), digital versatile Disc (Digital Video Disc-Read Only Memory), or other optical media), may be provided. In such cases, each drive may be coupled to bus 418 via one or more data medium interfaces. Storage 428 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
Programs 436 having a set (at least one) of program modules 426 may be stored, for example, in storage 428, such program modules 426 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 426 typically carry out the functions and/or methods of the embodiments described herein.
The computer device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, camera, display 424, etc.), one or more devices that enable a user to interact with the computer device 412, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 412 to communicate with one or more other computing devices. Such communication may occur through an Input/Output (I/O) interface 422. Moreover, computer device 412 may also communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network Wide Area Network, a WAN, and/or a public network such as the internet via network adapter 420. As shown, network adapter 420 communicates with other modules of computer device 412 over bus 418. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 412, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk array (Redundant Arrays of INDEPENDENT DISKS, RAID) systems, tape drives, data backup storage systems, and the like.
The processor 416 executes various functional applications and data processing by running a program stored in the storage device 428, for example, to implement the processing method of point cloud data provided by the above-described embodiment of the present invention.
That is, the processing unit realizes when executing the program: calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data; calculating the number of unilateral blocks matched with the point cloud data according to the total coverage, the ideal unilateral size of a single DSM and the DSM resolution; dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree; and calculating the partition DSM corresponding to each point cloud partition, and splicing the partition DSM to obtain the complete DSM corresponding to the point cloud data.
Example six
A sixth embodiment of the present invention further provides a computer storage medium storing a computer program, where the computer program when executed by a computer processor is configured to perform the method for processing point cloud data according to any one of the foregoing embodiments of the present invention: namely: calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data; calculating the number of unilateral blocks matched with the point cloud data according to the total coverage, the ideal unilateral size of a single DSM and the DSM resolution; dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree; and calculating the partition DSM corresponding to each point cloud partition, and splicing the partition DSM to obtain the complete DSM corresponding to the point cloud data.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having 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 ((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. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.

Claims (16)

1. The processing method of the point cloud data is characterized by comprising the following steps of:
calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data;
Calculating the number of unilateral blocks matched with the point cloud data according to the overall coverage, the ideal unilateral size of the monolithic digital surface model DSM and the DSM resolution;
Dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree;
Calculating partition DSMs corresponding to the point cloud partitions respectively, and splicing the partition DSMs to obtain a complete DSM corresponding to the point cloud data;
The dividing the point cloud data into a plurality of point cloud segments according to the overall coverage area, the single-side segment number and the preset overlapping degree includes:
According to the overall coverage range and the unilateral block number, calculating a first horizontal coordinate range corresponding to each point cloud block;
Updating each first horizontal coordinate range into a second horizontal coordinate range according to the preset overlapping degree;
and distributing each point cloud data point in the point cloud data to the corresponding point cloud partition according to a second horizontal coordinate range respectively corresponding to each point cloud partition.
2. The method of claim 1, further comprising, prior to calculating a single-sided block number matching the point cloud data based on the overall coverage, an ideal single-sided size of a monolithic DSM, and a DSM resolution:
And calculating a ground sampling interval (GSD) according to image acquisition parameters matched with the point cloud data, and determining the DSM resolution according to the GSD.
3. The method of claim 1, further comprising, prior to partitioning the point cloud data into a plurality of point cloud tiles according to the overall coverage, the single-sided tile count, and a preset degree of overlap:
calculating the thickness of the point cloud data according to the total data amount of the point cloud data points included in the point cloud data and the total coverage area;
and calculating the overlapping degree according to the density.
4. The method according to claim 1, wherein calculating the number of single-sided tiles matching the point cloud data based on the overall coverage, the ideal single-sided size of the monolithic DSM, and the DSM resolution, comprises:
according to the formula: Calculating a unilateral block number spilt _tiles matched with the point cloud data;
Wherein S is the total coverage, S is the product of the horizontal width and the horizontal length of the point cloud data, tile_max_size is the ideal single-side size of a single DSM, and resolution is the DSM resolution.
5. A method according to claim 3, wherein calculating the overlap from the consistency comprises:
according to the formula: Calculating the overlapping degree overlapp _step; pointcloud _density is the consistency, A is a preset empirical constant.
6. The method of claim 1, wherein computing a partition DSM corresponding to each of the point cloud partitions, respectively, comprises:
performing interpolation processing on the point cloud data points in each point cloud partition by adopting a preset interpolation algorithm;
And generating a partitioned DSM corresponding to each first horizontal coordinate range according to the point cloud data points and the interpolation points falling in each first horizontal coordinate range.
7. The method of claim 6, further comprising, after obtaining a complete DSM corresponding to the point cloud data:
re-blocking the complete DSM by using each second horizontal coordinate range to obtain a plurality of new blocked DSMs;
Carrying out orthorectification and color balance adjustment on each new partitioned DSM according to the matched aerial image, and calculating partitioned digital forward image DOM corresponding to each new partitioned DSM respectively;
And nesting, fusing and splicing each block DOM to obtain a complete DOM corresponding to the point cloud data.
8. A point cloud data processing apparatus, comprising:
The coverage calculating module is used for calculating the overall coverage of the point cloud data according to the horizontal coordinates of each point cloud data point in the point cloud data;
The block number calculation module is used for calculating the single-side block number matched with the point cloud data according to the overall coverage, the ideal single-side size of the single-block digital surface model DSM and the DSM resolution;
The point cloud data dividing module is used for dividing the point cloud data into a plurality of point cloud blocks according to the overall coverage area, the single-side block number and the preset overlapping degree;
The DSM generation module is used for calculating the partition DSMs respectively corresponding to the point cloud partitions, and splicing the partition DSMs to obtain a complete DSM corresponding to the point cloud data;
The point cloud data dividing module is specifically configured to:
According to the overall coverage range and the unilateral block number, calculating a first horizontal coordinate range corresponding to each point cloud block;
Updating each first horizontal coordinate range into a second horizontal coordinate range according to the preset overlapping degree;
and distributing each point cloud data point in the point cloud data to the corresponding point cloud partition according to a second horizontal coordinate range respectively corresponding to each point cloud partition.
9. The apparatus of claim 8, further comprising a DSM resolution determination module to:
Before calculating the number of single-side blocks matched with the point cloud data according to the overall coverage, the ideal single-side size of a single DSM and the DSM resolution, calculating a ground sampling interval GSD according to image acquisition parameters matched with the point cloud data, and determining the DSM resolution according to the GSD.
10. The apparatus as recited in claim 8, further comprising:
The density calculating unit is used for calculating the density of the point cloud data according to the total data amount of the point cloud data points included in the point cloud data and the total coverage area before the point cloud data are divided into a plurality of point cloud blocks according to the total coverage area, the single-side block number and the preset overlapping degree;
and the overlapping degree calculating unit is used for calculating the overlapping degree according to the density.
11. The device according to claim 8, wherein the block number calculation module is specifically configured to:
according to the formula: Calculating a unilateral block number spilt _tiles matched with the point cloud data;
Wherein S is the total coverage, S is the product of the horizontal width and the horizontal length of the point cloud data, tile_max_size is the ideal single-side size of a single DSM, and resolution is the DSM resolution.
12. The apparatus according to claim 10, wherein the overlap calculating unit is specifically configured to:
according to the formula: Calculating the overlapping degree overlapp _step; pointcloud _density is the consistency, A is a preset empirical constant.
13. The device according to claim 8, wherein the block number calculation module is specifically configured to:
performing interpolation processing on the point cloud data points in each point cloud partition by adopting a preset interpolation algorithm;
And generating a partitioned DSM corresponding to each first horizontal coordinate range according to the point cloud data points and the interpolation points falling in each first horizontal coordinate range.
14. The apparatus of claim 13, further comprising a DOM generation module to:
after obtaining the complete DSM corresponding to the point cloud data, re-blocking the complete DSM by using each second horizontal coordinate range to obtain a plurality of new blocked DSMs;
Carrying out orthorectification and color balance adjustment on each new partitioned DSM according to the matched aerial image, and calculating partitioned digital forward image DOM corresponding to each new partitioned DSM respectively;
And nesting, fusing and splicing each block DOM to obtain a complete DOM corresponding to the point cloud data.
15. A computer device, the computer device comprising:
one or more processors;
a storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method of processing point cloud data as claimed in any of claims 1-7.
16. A computer storage medium having stored thereon a computer program, which when executed by a processor implements a method of processing point cloud data according to any of claims 1-7.
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