CN115082641A - Point cloud rasterization method and device based on gridding multi-neighborhood interpolation - Google Patents

Point cloud rasterization method and device based on gridding multi-neighborhood interpolation Download PDF

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CN115082641A
CN115082641A CN202210995957.9A CN202210995957A CN115082641A CN 115082641 A CN115082641 A CN 115082641A CN 202210995957 A CN202210995957 A CN 202210995957A CN 115082641 A CN115082641 A CN 115082641A
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grid
point cloud
pixel
image
point
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CN115082641B (en
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葛慧斌
王宇翔
廖通逵
张金金
沈均平
巴晓娟
路聚峰
杨娜
管占磊
王世超
张纪华
宋明明
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Aerospace Hongtu Information Technology Co Ltd
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Abstract

The application provides a point cloud rasterization method and device based on gridding multi-neighborhood interpolation, which relate to the technical field of point cloud data processing, and the method comprises the following steps: rasterizing all point cloud data to obtain a raster image; performing gridding processing on the grid image to obtain a gridded grid image, and acquiring and storing the point cloud number and the point cloud serial number of each grid in the gridded grid image; for each pixel of the grid image, acquiring a grid to which the pixel belongs; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing; and taking the elevation value of each pixel as a pixel value to obtain a point cloud grid image. The method and the device improve the processing speed and precision of point cloud rasterization.

Description

Point cloud rasterization method and device based on gridding multi-neighborhood interpolation
Technical Field
The application relates to the technical field of point cloud data processing, in particular to a point cloud rasterization method and device based on gridding multi-neighborhood interpolation.
Background
Most of the existing point cloud rasterization interpolation methods are directed at laser radar point clouds, the density of the laser radar point clouds is usually very high and is far higher than the resolution of rasterized images, so that the aim of the method is mainly to duplicate the point clouds, and each grid unit only keeps one point or performs distance interpolation by all the points in the grid unit.
The point cloud in the present application does not refer to a laser radar point cloud, but is a dense point cloud obtained by dense matching of stereo satellite image data or aerial shooting image data, and generally the dense point cloud has a requirement of being output as a grid Digital Surface Model (DSM), even if the point cloud obtained by pixel-by-pixel dense matching is at most consistent with the grid resolution to be output, and because the image has high floor and shadow occlusion, cloud zone occlusion, water area and other areas where the dense point cloud cannot be obtained, it can be seen that the point clouds resulting from such dense matching are not uniformly distributed, there are grid cells in which there is at most one point, however, some grid cells do not have any point, even a flaky grid cell area does not have any point, the existing laser radar point cloud rasterization method is not applicable necessarily, and scabs and holes also appear in the rasterized DSM.
Disclosure of Invention
In view of the above, the present application provides a method and an apparatus for rasterizing a point cloud based on gridding multi-neighborhood interpolation, so as to solve the above technical problem.
In a first aspect, an embodiment of the present application provides a point cloud rasterization method based on gridding multi-neighborhood interpolation, which is characterized by including:
rasterizing all point cloud data to obtain a raster image;
performing gridding processing on the grid image to obtain a gridded grid image, and acquiring and storing the point cloud number and the point cloud serial number of each grid in the gridded grid image;
for each pixel of the grid image, acquiring a grid to which the pixel belongs; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing;
and taking the elevation value of each pixel as a pixel value to obtain a point cloud grid image.
Further, rasterizing the point cloud data to obtain a raster image; the method comprises the following steps:
obtaining geodetic coordinates of each point cloud
Figure P_220815073815040_040987001
Wherein, in the step (A),
Figure P_220815073815072_072185002
as a result of the longitude, the number of times,
Figure P_220815073815087_087800003
as the latitude, the number of years of the latitude,
Figure P_220815073815119_119100004
is an elevation value;
counting all point clouds
Figure P_220815073815139_139524001
Coordinates of the objectMinimum value of (2)
Figure P_220815073815171_171315002
And maximum value
Figure P_220815073815186_186924003
，
Figure P_220815073815233_233827004
Minimum value of coordinates
Figure P_220815073815280_280689005
And maximum value
Figure P_220815073815296_296340006
；
Calculating the width of the grid image
Figure P_220815073815329_329465001
And height
Figure P_220815073815345_345599002
：
Figure P_220815073815376_376862001
Figure P_220815073815408_408132001
Wherein the content of the first and second substances,
Figure P_220815073815423_423737001
the resolution of the grid image;
create a size of
Figure P_220815073815455_455033001
The grid image of (2).
Further, gridding the grid image, and acquiring and storing the number of point clouds and the number of point clouds in each grid; the method comprises the following steps:
to image the grid
Figure P_220815073815470_470628001
Each pixel forms a grid to obtain a gridded grid image, and the side length of each grid is
Figure P_220815073815501_501862002
；
Number of meshes in column direction of gridded grid image
Figure P_220815073815517_517484001
And the number of grids in the row direction
Figure P_220815073815552_552166002
Comprises the following steps:
Figure P_220815073815567_567820001
Figure P_220815073815599_599044001
calculating two-dimensional coordinates in the grid where each point cloud is located
Figure P_220815073815630_630293001
And one-dimensional coordinate index
Figure P_220815073815661_661546002
：
Figure P_220815073815677_677153001
Figure P_220815073815708_708404001
Figure P_220815073815741_741077001
Create a size of
Figure P_220815073815788_788490001
The one-dimensional array grid _ pt _ num is used for recording the number of point clouds in each grid;
creating a first layer size of
Figure P_220815073815804_804115001
The second layer has a size of grid _ pt _ num [ ]gridpos]The two-dimensional array grid _ pt _ ids is used for recording the serial number of each point in the point cloud in each grid.
Further, for each pixel of the grid image, acquiring a grid to which the pixel belongs; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing; the method comprises the following steps:
obtaining the grids of the pixels in the gridding grid image by using the geographic coordinates of the pixels as central grids;
centered around a central grid
Figure P_220815073815835_835375001
Reading the number and the serial number of point clouds in 9 grids from a one-dimensional array grid _ pt _ num and a two-dimensional array grid _ pt _ ids by using 9 grids in the area;
judging whether the total number of all point clouds in the 9 grids is less than M or not, and if so, setting the elevation value of the pixel element as a preset invalid value; otherwise, carrying out interpolation processing on the elevation values of a preset number of point clouds in the 9 grids to obtain the elevation value of the pixel.
Further, obtaining a mesh of the pixel in the gridding grid image by using the geographic coordinate of the pixel; the method comprises the following steps:
for each pixel of the raster image, calculating the geographic coordinates of the center of each pixel
Figure P_220815073815881_881766001
And
Figure P_220815073815913_913011002
：
Figure P_220815073816101_101474001
Figure P_220815073816134_134169001
wherein the content of the first and second substances,
Figure P_220815073816165_165953001
is the column coordinate of the picture element,
Figure P_220815073816197_197212002
is the row coordinate of the pixel;
using geographical coordinates of the centre of each pixel
Figure P_220815073816212_212797001
And
Figure P_220815073816244_244072002
and calculating the grid coordinates of the center of the pixel element in the gridding grid image so as to obtain the grid to which the pixel element belongs.
Further, performing interpolation processing on the elevation values of a preset number of point clouds in the 9 grids to obtain the elevation value of the pixel, including:
aiming at each point cloud in 9 grids, directly acquiring the geographic coordinates of the point cloud from the point cloud data by using the point cloud serial number;
geographic coordinate and pixel center geographic coordinate based on point cloud
Figure P_220815073816259_259702001
Calculating the geographic distance between the two;
sequencing the point clouds according to the sequence of the geographic distances from small to large;
acquiring the elevation values of the first M point clouds after sorting, and calculating the elevation value Z of the pixel by using the geographical distance between the M point clouds and the center of the pixel:
Figure P_220815073816290_290963001
wherein the content of the first and second substances,
Figure P_220815073816322_322191001
the elevation value of the mth point cloud is represented,
Figure P_220815073816342_342175002
the geographic distance between the mth point cloud and the center of the pixel is obtained.
Further, the method further comprises:
step S1: acquiring all pixels with pixel values not being invalid values from the point cloud raster image;
step S2: forming a geometric object by using the coordinates of all the pixels, wherein the geometric object is an effective boundary of the point cloud grid image;
step S3: for each pixel of which the pixel value of the point cloud raster image is an invalid value, judging whether the pixel is in an effective boundary, if not, not processing, otherwise, entering the step S4;
step S4: obtaining the grids of the pixels in the gridding grid image by using the geographic coordinates of the pixels as central grids;
step S5: centered on a central grid
Figure P_220815073816373_373959001
Within a region
Figure P_220815073816405_405193002
A grid reading from the one-dimensional array grid _ pt _ num and the two-dimensional array grid _ pt _ ids
Figure P_220815073816436_436465003
The number and serial number of the point clouds in each grid;
Figure P_220815073816467_467692004
is 4;
step S6: judgment of
Figure P_220815073816483_483332001
Whether the total number of all point clouds in each grid is less than M or not, if so, determining whether the total number of all point clouds in each grid is less than M or not
Figure P_220815073816514_514567002
Then, the process proceeds to step S5; otherwise, it is to
Figure P_220815073816533_533090003
And carrying out interpolation processing on the elevation values of the point clouds in the preset number in each grid to obtain the elevation value of the pixel.
In a second aspect, an embodiment of the present application provides a point cloud rasterization apparatus based on gridding multi-neighborhood interpolation, including:
the rasterization processing unit is used for rasterizing all the point cloud data to obtain a raster image;
the grid processing unit is used for carrying out grid processing on the grid image to obtain a grid image, and acquiring and storing the point cloud number and the point cloud serial number of each grid in the grid image;
the interpolation unit is used for acquiring the grids to which each pixel of the raster image belongs; utilizing the number and the elevation value of point clouds in the belonging grid and the peripheral grids to obtain the elevation value of each pixel through interpolation processing;
and the point cloud raster image generating unit is used for taking the elevation value of each pixel as a pixel value to obtain a point cloud raster image.
In a third aspect, an embodiment of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the point cloud rasterization method based on gridding multi-neighborhood interpolation.
In a fourth aspect, the present application provides a computer-readable storage medium storing computer instructions, which when executed by a processor, implement the point cloud rasterization method based on gridding multi-neighborhood interpolation of the present application.
The method and the device can quickly rasterize the densely matched non-uniform discontinuous point cloud into a Digital Surface Model (DSM), and improve the processing speed and precision.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a point cloud rasterization method based on gridding multi-neighborhood interpolation provided in the embodiment of the present application;
fig. 2 is a functional structure diagram of a point cloud rasterization apparatus based on gridding multi-neighborhood interpolation provided in the embodiment of the present application;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
First, the design idea of the embodiment of the present application is briefly introduced.
Aiming at the problem that most of the existing point cloud rasterization methods are directed at high-density laser point cloud data and are not suitable for rasterization of point cloud data obtained by densely matching satellite stereo image data and aerial shooting image data, the application provides a point cloud rasterization method based on gridding multi-neighborhood interpolation, which can quickly rasterize densely matched non-uniform non-continuous point clouds into a Digital Surface Model (DSM), avoid that the interpolation of each pixel needs to traverse all the point clouds, and ensure that no cavity (invalid value) exists in the rasterized data.
After introducing the application scenario and the design idea of the embodiment of the present application, the following describes a technical solution provided by the embodiment of the present application.
As shown in fig. 1, an embodiment of the present application provides a point cloud rasterization method based on gridding multi-neighborhood interpolation, including:
step 101: rasterizing the point cloud data to obtain a raster image;
the point cloud data is a set of three-dimensional coordinates of a plurality of point clouds, each point cloud has a serial number in the whole point cloud data, and the three-dimensional coordinates are
Figure P_220815073816564_564855001
Wherein, in the step (A),
Figure P_220815073816580_580497002
as a result of the longitude, the number of times,
Figure P_220815073816611_611744003
in the case of the latitude, the latitude is,
Figure P_220815073816627_627360004
in order to be the elevation value,
Figure P_220815073816642_642997005
the geographic coordinates of the point cloud; the point cloud rasterization of the application is to rasterize the point cloud data into a scene image data,
Figure P_220815073816674_674277006
is converted into the image coordinates by the image sensor,
Figure P_220815073816689_689865007
and converting the gray scale value into the gray scale value of the image.
Counting all point clouds
Figure P_220815073816721_721126001
Minimum value of coordinates
Figure P_220815073816738_738152002
And maximum value
Figure P_220815073816769_769516003
，
Figure P_220815073816801_801214004
Minimum value of coordinates
Figure P_220815073816816_816359005
And maximum value
Figure P_220815073816847_847601006
；
Calculating the width of the grid image
Figure P_220815073816879_879308001
And height
Figure P_220815073816894_894963002
：
Figure P_220815073816925_925723001
Figure P_220815073816957_957465001
Wherein, the first and the second end of the pipe are connected with each other,
Figure P_220815073816972_972602001
the resolution of the grid image;
create a size of
Figure P_220815073817003_003862001
The raster image of (1) band number, data type float, width
Figure P_220815073817035_035077002
High is
Figure P_220815073817066_066841003
Geographic coordinate range of
Figure P_220815073817081_081964004
The projection is set according to the output requirement, and the resolution is
Figure P_220815073817113_113672005
。
Step 102: performing gridding processing on the grid image to obtain a gridded grid image, and acquiring and storing the point cloud number and the point cloud serial number of each grid in the gridded grid image;
to image the grid
Figure P_220815073817146_146879001
Each pixel forms a grid to obtain a gridded grid image, and the side length of each grid is
Figure P_220815073817178_178182002
；
Number of meshes in column direction of gridded grid image
Figure P_220815073817193_193789001
And number of grids in the row direction
Figure P_220815073817225_225031002
Comprises the following steps:
Figure P_220815073817256_256271001
Figure P_220815073817287_287529001
thereby, the grid image is divided
Figure P_220815073817318_318763001
A grid of size
Figure P_220815073817355_355385002
(ii) a Traversing the whole point cloud data, and calculating two-dimensional coordinates in a grid where each point cloud is located
Figure P_220815073817371_371027003
And one-dimensional coordinate index
Figure P_220815073817402_402290004
：
Figure P_220815073817433_433513001
Figure P_220815073817464_464768001
Figure P_220815073817496_496014001
Creating a one-dimensional array grid _ pt _ num with the size of gridcol grid, and recording the number of point clouds in each grid; and creating a first layer of size gridcol gridrow and a second layer of size grid _ pt _ num [ 2 ]gridpos]Two-dimensional ofAnd the group grid _ pt _ ids is used for recording the serial number of each point in each grid in the point cloud, and the purpose of recording the serial number is to quickly acquire the geographic coordinates of the point cloud from the point cloud data according to the serial number.
Step 103: for each pixel of the grid image, acquiring a grid to which the pixel belongs; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing;
for each pixel of the raster image, calculating the geographic coordinates of the center of each pixel
Figure P_220815073817527_527274001
And
Figure P_220815073817560_560019002
：
Figure P_220815073817591_591251001
Figure P_220815073817622_622499001
wherein the content of the first and second substances,
Figure P_220815073817653_653752001
is the column coordinate of the picture element,
Figure P_220815073817669_669377002
is the row coordinate of the pixel;
using geographical coordinates of the centre of each pixel
Figure P_220815073817700_700621001
And
Figure P_220815073817733_733289002
and calculating the grid coordinates of the pixel center in the gridding grid image, thereby obtaining the grid to which the pixel belongs as a center grid.
Centered on a central grid
Figure P_220815073817749_749435001
Reading the number and the serial number of point clouds in 9 grids from a one-dimensional array grid _ pt _ num and a two-dimensional array grid _ pt _ ids by using 9 grids in the area; since the space data structures grid _ pt _ num and grid _ pt _ ids are recorded before, the statistics does not need to traverse all the point clouds, but are directly read based on the grids, and a large amount of traversal time is saved.
Judging whether the total number of all point clouds in 9 grids is less than M (M is usually 8), if so, indicating that no point clouds exist around the center of the pixel, and setting the elevation value of the pixel as a preset invalid value; otherwise, aiming at each point cloud in the 9 grids and each point cloud in the 9 grids, directly acquiring the geographic coordinates of the point cloud from the point cloud data by using the point cloud serial number; point cloud-based geographic coordinate and pixel center geographic coordinate
Figure P_220815073817780_780714001
Calculating the geographic distance between the two; sequencing the point clouds according to the sequence of the geographic distances from small to large; acquiring the elevation values of the first M point clouds after sorting, and calculating the elevation value of the pixel by using the geographical distance between the M point clouds and the center of the pixelZ：
Figure P_220815073817811_811942001
Wherein the content of the first and second substances,
Figure P_220815073817843_843206001
the elevation value of the mth point cloud is represented,
Figure P_220815073817858_858824002
and the geographic distance between the mth point cloud and the center of the pixel.
Step 104: taking the elevation value of each pixel as a pixel value to obtain a point cloud grid image;
because point cloud data obtained by dense matching on an image has unevenness and discontinuity, a point cloud raster image usually has many holes, and therefore hole filling processing after interpolation is needed. The method comprises the following specific steps:
step S1: acquiring all pixels of which the pixel values are not invalid values from the point cloud grid image;
step S2: forming a geometric object by using the coordinates of all the pixels, wherein the geometric object is an effective boundary of the point cloud grid image;
step S3: for each pixel of which the pixel value of the point cloud raster image is an invalid value, judging whether the pixel is in an effective boundary, if not, not processing, otherwise, entering the step S4;
step S4: obtaining the grids of the pixels in the gridding grid image by using the geographic coordinates of the pixels as central grids;
step S5: centered on a central grid
Figure P_220815073817890_890076001
Within a region
Figure P_220815073817921_921326002
A grid reading from the one-dimensional array grid _ pt _ num and the two-dimensional array grid _ pt _ ids
Figure P_220815073817938_938859003
The number and serial number of the point clouds in each grid;
Figure P_220815073817970_970637004
is 4;
step S6: judgment of
Figure P_220815073817986_986268001
Whether the total number of all point clouds in each grid is less than M or not, if so, determining whether the total number of all point clouds in each grid is less than M or not
Figure P_220815073818017_017511002
Then, the process proceeds to step S5; otherwise, it is to
Figure P_220815073818048_048771003
And carrying out interpolation processing on the elevation values of the point clouds in the preset number in each grid to obtain the elevation value of the pixel.
The adopted test data is point cloud data obtained by densely matching satellite three-dimensional image data, and the number of the points is one hundred million, two thousand and five million; obtaining a rasterized image rendering image by using the method, wherein the rasterization consumes 202 seconds; however, the conventional method for constructing the TIN interpolation by using the point cloud consumes about one hour.
Based on the foregoing embodiments, an embodiment of the present application provides a point cloud rasterization apparatus based on gridding multi-neighborhood interpolation, and referring to fig. 2, the point cloud rasterization apparatus 200 based on gridding multi-neighborhood interpolation provided by the embodiment of the present application at least includes:
a rasterization processing unit 201, configured to perform rasterization processing on all point cloud data to obtain a raster image;
a gridding processing unit 202, configured to perform gridding processing on the grid image to obtain a gridded grid image, and acquire and store the point cloud number and the point cloud number of each grid in the gridded grid image;
the interpolation unit 203 is configured to obtain, for each pixel of the raster image, a mesh to which the pixel belongs; utilizing the number and the elevation value of point clouds in the belonging grid and the peripheral grids to obtain the elevation value of each pixel through interpolation processing;
and the point cloud raster image generating unit 204 is configured to obtain a point cloud raster image by using the elevation value of each pixel as a pixel value.
It should be noted that the principle of the point cloud rasterization apparatus 200 based on the gridding multi-neighborhood interpolation provided in the embodiment of the present application for solving the technical problem is similar to the point cloud rasterization method based on the gridding multi-neighborhood interpolation provided in the embodiment of the present application, and therefore, the implementation of the point cloud rasterization apparatus 200 based on the gridding multi-neighborhood interpolation provided in the embodiment of the present application may refer to the implementation of the point cloud rasterization method based on the gridding multi-neighborhood interpolation provided in the embodiment of the present application, and repeated parts are not described again.
As shown in fig. 3, an electronic device 300 provided in the embodiment of the present application at least includes: the system comprises a processor 301, a memory 302 and a computer program stored on the memory 302 and capable of running on the processor 301, wherein the processor 301 implements the point cloud rasterization method based on gridding multi-neighborhood interpolation provided by the embodiment of the application when executing the computer program.
The electronic device 300 provided by the embodiment of the present application may further include a bus 303 connecting different components (including the processor 301 and the memory 302). Bus 303 represents one or more of any of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.
The Memory 302 may include readable media in the form of volatile Memory, such as Random Access Memory (RAM) 3021 and/or cache Memory 3022, and may further include Read Only Memory (ROM) 3023.
The memory 302 may also include a program tool 3024 having a set (at least one) of program modules 3025, the program modules 3025 including, but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboard, remote control, etc.), with one or more devices that enable a user to interact with the electronic device 300 (e.g., cell phone, computer, etc.), and/or with any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., router, modem, etc.). Such communication may be through an Input/Output (I/O) interface 305. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network, such as the internet) via the Network adapter 306. As shown in FIG. 3, the network adapter 306 communicates with the other modules of the electronic device 300 via the bus 303. It should be understood that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, Redundant processors, external disk drive Arrays, disk array (RAID) subsystems, tape drives, and data backup storage subsystems, to name a few.
It should be noted that the electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
The embodiment of the application also provides a computer-readable storage medium, which stores computer instructions, and the computer instructions, when executed by a processor, implement the point cloud rasterization method based on the gridding multi-neighborhood interpolation provided by the embodiment of the application.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. A point cloud rasterization method based on gridding multi-neighborhood interpolation is characterized by comprising the following steps:
rasterizing the point cloud data to obtain a raster image;
performing gridding processing on the grid image to obtain a gridded grid image, and acquiring and storing the point cloud number and the point cloud serial number of each grid in the gridded grid image;
for each pixel of the grid image, acquiring a grid to which the pixel belongs; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing;
and taking the elevation value of each pixel as a pixel value to obtain a point cloud grid image.
2. The grid multi-neighborhood interpolation-based point cloud rasterization method according to claim 1, wherein the point cloud data is subjected to rasterization processing to obtain a grid image; the method comprises the following steps:
obtaining geodetic coordinates of each point cloud
Figure P_220815073810508_508233001
Wherein, in the step (A),
Figure P_220815073811093_093685002
as a result of the longitude, the number of times,
Figure P_220815073811109_109294003
in the case of the latitude, the latitude is,
Figure P_220815073811141_141474004
is an elevation value;
counting all point clouds
Figure P_220815073811157_157637001
Minimum value of coordinates
Figure P_220815073811188_188894002
And maximum value
Figure P_220815073811204_204525003
，
Figure P_220815073811235_235746004
Minimum value of coordinates
Figure P_220815073811251_251390005
And maximum value
Figure P_220815073811282_282644006
；
Calculating the width of a grid image
Figure P_220815073811298_298271001
And height
Figure P_220815073811330_330463002
：
Figure P_220815073811346_346612001
Figure P_220815073811377_377859001
Wherein the content of the first and second substances,
Figure P_220815073811393_393491001
the resolution of the grid image;
create a size of
Figure P_220815073811424_424736001
The grid image of (2).
3. The gridding multi-neighborhood interpolation-based point cloud rasterization method according to claim 2, wherein gridding processing is performed on a grid image to obtain and store the number of point clouds and the number of point clouds in each grid; the method comprises the following steps:
to image the grid
Figure P_220815073811440_440367001
Each pixel forms a grid to obtain a gridded grid image, and the side length of each grid is
Figure P_220815073811471_471593002
；
Number of meshes in column direction of gridded grid image
Figure P_220815073811487_487235001
And the number of grids in the row direction
Figure P_220815073811518_518489002
Comprises the following steps:
Figure P_220815073811551_551678001
Figure P_220815073811582_582928001
calculating two-dimensional coordinates in the grid of each point cloud
Figure P_220815073811614_614177001
And one-dimensional coordinate index
Figure P_220815073811629_629817002
：
Figure P_220815073811661_661049001
Figure P_220815073811692_692306001
Figure P_220815073811723_723557001
Create a size of
Figure P_220815073811756_756760001
The one-dimensional array grid _ pt _ num is used for recording the number of point clouds in each grid;
creating a first layer size of
Figure P_220815073811772_772395001
The second layer has a size of grid _ pt _ num [ ]gridpos]The two-dimensional array grid _ pt _ ids is used for recording the serial number of each point in the point cloud in each grid.
4. The method of claim 3, wherein for each pixel of the grid image, the grid to which it belongs is obtained; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing; the method comprises the following steps:
obtaining a grid of the pixel in the gridding grid image by using the geographic coordinate of the pixel as a central grid;
centered on a central grid
Figure P_220815073811803_803630001
Reading the number and the serial number of point clouds in 9 grids from a one-dimensional array grid _ pt _ num and a two-dimensional array grid _ pt _ ids by using 9 grids in the area;
judging whether the total number of all point clouds in the 9 grids is less than M or not, and if so, setting the elevation value of the pixel element as a preset invalid value; otherwise, carrying out interpolation processing on the elevation values of the point clouds with the preset number in the 9 grids to obtain the elevation value of the pixel.
5. The method of claim 4, wherein the geographic coordinates of the pixels are used to obtain the mesh to which the pixels belong in the gridded grid image; the method comprises the following steps:
for each pixel of the raster image, calculating the geographic coordinates of the center of each pixel
Figure P_220815073811834_834899001
And
Figure P_220815073811850_850484002
：
Figure P_220815073811881_881746001
Figure P_220815073811913_913000001
wherein the content of the first and second substances,
Figure P_220815073811946_946701001
is the column coordinate of the picture element,
Figure P_220815073811977_977946002
is the row coordinate of the pixel;
using geographical coordinates of the centre of each pixel
Figure P_220815073811993_993584001
And
Figure P_220815073812024_024834002
and calculating the grid coordinates of the center of the pixel in the gridding grid image so as to obtain the grid to which the pixel belongs.
6. The method for rasterizing a point cloud based on gridding multi-neighborhood interpolation according to claim 5, wherein interpolating the elevation values of a preset number of point clouds in 9 grids to obtain the elevation value of the pixel comprises:
aiming at each point cloud in 9 grids, directly acquiring the geographic coordinates of the point cloud from the point cloud data by using the point cloud serial number;
geographic coordinate and pixel center geographic coordinate based on point cloud
Figure P_220815073812056_056064001
Calculating the geographic distance between the two;
sequencing the point clouds according to the sequence of the geographic distances from small to large;
acquiring the elevation values of the front M point clouds after sorting, and calculating the elevation value Z of the pixel by using the geographical distance between the M point clouds and the center of the pixel:
Figure P_220815073812087_087354001
wherein the content of the first and second substances,
Figure P_220815073812102_102967001
the elevation value of the mth point cloud is represented,
Figure P_220815073812137_137579002
and the geographic distance between the mth point cloud and the center of the pixel.
7. The method of rasterizing a point cloud based on gridded multi-neighborhood interpolation of claim 6 further comprising:
step S1: acquiring all pixels with pixel values not being invalid values from the point cloud raster image;
step S2: forming a geometric object by using the coordinates of all the pixels, wherein the geometric object is an effective boundary of the point cloud grid image;
step S3: for each pixel of which the pixel value of the point cloud raster image is an invalid value, judging whether the pixel is in an effective boundary, if not, not processing, otherwise, entering the step S4;
step S4: obtaining the grids of the pixels in the gridding grid image by using the geographic coordinates of the pixels as central grids;
step S5: centered on a central grid
Figure P_220815073812153_153731001
Within a region
Figure P_220815073812185_185003002
A grid reading from the one-dimensional array grid _ pt _ num and the two-dimensional array grid _ pt _ ids
Figure P_220815073812216_216240003
The number and serial number of the point clouds in each grid;
Figure P_220815073812231_231867004
is 4;
step S6: judgment of
Figure P_220815073812263_263133001
Whether the total number of all point clouds in each grid is less than M or not, if so, determining whether the total number of all point clouds in each grid is less than M or not
Figure P_220815073812294_294350002
Then, the process proceeds to step S5; otherwise, it is to
Figure P_220815073812309_309998003
Interpolation processing is carried out on the elevation values of the point clouds with preset number in each gridAnd obtaining the elevation value of the pixel.
8. A point cloud rasterization device based on gridding multi-neighborhood interpolation is characterized by comprising the following components:
the rasterization processing unit is used for rasterizing the point cloud data to obtain a raster image;
the grid processing unit is used for carrying out grid processing on the grid image to obtain a grid image, and acquiring and storing the point cloud number and the point cloud serial number of each grid in the grid image;
the interpolation unit is used for acquiring the grids to which each pixel of the raster image belongs; utilizing the point cloud number and the elevation value in the affiliated grid and the peripheral grids thereof to obtain the elevation value of each pixel through interpolation processing;
and the point cloud raster image generating unit is used for taking the elevation value of each pixel as a pixel value to obtain a point cloud raster image.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the method of point cloud rasterization based on gridded multi-neighborhood interpolation of any one of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, implement the grid multi-neighborhood interpolation based point cloud rasterization method of any one of claims 1 to 7.
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