CN113096198B - Bidirectional point cloud attribute prediction compression method, device, equipment and medium - Google Patents
Bidirectional point cloud attribute prediction compression method, device, equipment and medium Download PDFInfo
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Abstract
The invention discloses a bidirectional point cloud attribute prediction compression method, a device, equipment and a medium, wherein the method comprises the following steps: respectively calculating the standard deviation of the point cloud data on a geometric coordinate axis; determining a spatial bias coefficient according to the standard deviation; performing Morton coding ordering or Hilbert coding ordering on the geometric information of the point cloud data, and determining a Morton order or a Hilbert order; performing attribute prediction according to the Morton sequence or the Hilbert sequence combined with the sequence number, and determining an attribute predicted value; and performing attribute prediction compression on the point cloud data according to the attribute prediction value. The invention reduces the uncertainty caused by setting the space bias coefficient according to personal experience in the past, can more accurately find the closest point in the real geometric space, reduces the loss in the attribute compression of point cloud data, and can be widely applied to the technical field of point cloud data processing.
Description
Technical Field
The invention relates to the technical field of point cloud data processing, in particular to a bidirectional point cloud attribute prediction compression method, device, equipment and medium.
Background
The Point Cloud (Point Cloud) is an expression form of a three-dimensional object or scene, and is composed of a set of discrete points which are randomly distributed in space and express the spatial structure and surface attributes of the three-dimensional object or scene. The points in the point cloud include some additional attributes, such as color, reflectivity, etc., in addition to the geometric coordinates. There is no specified spatial connection or order relationship between the points. The point cloud data is mainly divided into three types according to the acquisition ways: 1. static point cloud: the object is stationary and the apparatus for acquiring the point cloud is also stationary. 2. Dynamic point cloud: the object is moving and the apparatus that acquires the point cloud is stationary. 3, dynamically acquiring point cloud: the equipment that acquires the point cloud is moving.
The point cloud data is widely applied to automatic driving, high-precision maps, virtual reality and the like, but because the point cloud data is composed of tens of thousands of points to hundreds of millions of three-dimensional points and corresponding attribute information, serious challenges are brought to subsequent storage, processing and transmission display, and the storage resources and transmission bandwidth at present are difficult to bear, so that the high-efficiency compression technology is indispensable to store and transmit the point cloud data.
At present, a Moving Picture Expert Group (MPEG) belonging to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) started a Standardization project for a Point Cloud coding solution in 2017, a core technology of the MPEG is called Point Cloud Compression (PCC), and a national digital Audio and Video coding Standard working Group (Audio Video coding Standard Workgroup of China) also starts the Point Cloud Compression. The mainstream schemes are video coding-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC), respectively.
The video-based point cloud compression method mainly projects point cloud data from a three-dimensional space to a two-dimensional space, and then compresses a two-dimensional image by using a traditional video compression method. The point cloud compression method based on geometry firstly aims at the geometric information (x) of the point cloud data aiming at the attribute information i ,y i ,z i ) And carrying out Morton code or Hilbert code sequencing, and traversing the coded points in a certain search range of the current points to be coded according to the Morton sequence or the Hilbert sequence. However, the spatial bias coefficient of the method is theta, which needs to be set according to own experience, and has great uncertainty, and secondly, only points in front of the morton sequence or the hilbert sequence of the current point are considered in a unidirectional mode, and the two-way prediction is not carried out by a B frame and a P frame in the traditional video compression, so that the closest point in the real geometric space cannot be accurately found, and unnecessary loss is brought to the attribute compression of the point cloud.
Disclosure of Invention
In view of this, embodiments of the present invention provide a bidirectional point cloud attribute prediction compression method, apparatus, device, and medium, so as to implement more accurate compression of point cloud data.
In one aspect, the invention provides a bidirectional point cloud attribute prediction compression method, which comprises the following steps:
respectively calculating the standard deviation of the point cloud data on a geometric coordinate axis;
determining a spatial bias coefficient according to the standard deviation;
performing Morton coding ordering or Hilbert coding ordering on the geometric information of the point cloud data, and determining a Morton order or a Hilbert order;
performing attribute prediction according to the Morton sequence or the Hilbert sequence combined with the sequence number, and determining an attribute predicted value;
and performing attribute prediction compression on the point cloud data according to the attribute prediction value.
Further, the calculating the standard deviation of the point cloud data on the geometric coordinate axis respectively includes:
determining three one-dimensional arrays according to the X, Y and Z coordinates of the point cloud data;
determining standard deviations of the point cloud data X, Y and Z according to the one-dimensional array;
the calculation formula of the standard deviation is as follows:
wherein S is standard deviation, n is the number of elements in the one-dimensional array, i is positive integer, A i Is the ith element in the one-dimensional array, and mu is the mean value of all the elements in the one-dimensional array.
Further, the determining a spatial bias coefficient according to the standard deviation includes:
determining the coordinate dispersion degree of the point cloud data according to the standard deviation;
and determining the spatial bias coefficient according to the coordinate discrete degree.
Further, the performing attribute prediction according to the morton sequence or the hilbert sequence combined sequence number to determine an attribute prediction value includes:
according to the Morton sequence or the Hilbert sequence, performing unidirectional attribute prediction on odd points, and determining a first attribute prediction value corresponding to the odd points;
and according to the Morton sequence or the Hilbert sequence, after all odd points are predicted, performing bidirectional attribute prediction on the even points, and determining a second attribute predicted value corresponding to the even points.
Further, the specific step of performing bidirectional attribute prediction on the even points after all odd points are predicted according to the morton sequence or the hilbert sequence, and determining the second attribute predicted value corresponding to the even points includes:
determining the search range of the bidirectional attribute prediction as all points smaller than the even points and odd points larger than the even points;
calculating and determining the distance of each adjacent point according to the search range predicted by the bidirectional attribute;
calculating and determining a weight of a reference point according to the search range of the bidirectional attribute prediction, wherein the reference point is at least one point with the minimum distance selected from the adjacent points;
and determining a second attribute predicted value of the odd point according to the search range usage of the bidirectional attribute prediction.
Further, the performing attribute prediction compression on the point cloud data according to the attribute prediction value includes:
determining an attribute residual error of the point cloud data according to the attribute predicted value;
performing quantization processing according to the attribute residual of the point cloud data, and determining a residual value after quantization;
and performing coding processing according to the quantized residual error value to determine a binary code.
On the other hand, the invention also discloses a bidirectional point cloud attribute prediction compression device, which comprises the following modules:
the first module is used for respectively calculating the standard deviation of the point cloud data on the geometric coordinate axis;
a second module, configured to determine a spatial bias coefficient according to the standard deviation;
the third module is used for conducting Morton coding sequencing or Hilbert coding sequencing on the geometric information of the point cloud data and determining the Morton sequence or the Hilbert sequence;
a fourth module, configured to perform attribute prediction according to the morton sequence or the hilbert sequence combined with the sequence number, and determine an attribute prediction value;
and the fifth module is used for performing attribute prediction compression on the point cloud data according to the attribute prediction value.
On the other hand, the invention also discloses an electronic device, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
In another aspect, the present invention also discloses a computer readable storage medium, which stores a program, which is executed by a processor to implement the method as described above.
In another aspect, the present invention also discloses a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
Compared with the prior art, the technical scheme adopted by the invention has the following technical effects: according to the embodiment of the invention, the standard deviation of the point cloud data on the geometric coordinate axis is respectively calculated, and the spatial bias coefficient is determined according to the standard deviation, so that the uncertainty caused by setting the spatial bias coefficient according to personal experience in the past can be reduced; in addition, according to the embodiment of the invention, Morton coding ordering or Hilbert coding ordering is carried out on the geometric information of the point cloud data, a Morton order or a Hilbert order is determined, attribute prediction is carried out according to the Morton order or the Hilbert order and the sequence number, and an attribute predicted value is determined, so that the closest point in a real geometric space can be found more accurately, and the loss in attribute compression of point cloud data is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an algorithmic flow diagram of an embodiment of the present invention;
FIG. 2 is a first frame Ford _01_1mm-0100 in a dynamically acquired point cloud data Ford _01_1mm dataset;
FIG. 3 is a coordinate statistical chart of Ford _01_1mm-0100 data in the direction of X coordinate axis;
FIG. 4 is a coordinate statistical chart of Ford _01_1mm-0100 data in the Y coordinate axis direction;
FIG. 5 is a coordinate statistical chart of Ford _01_1mm-0100 data in the direction of the Z coordinate axis;
FIG. 6 is a schematic diagram illustrating the prediction sequence of odd-numbered points in the point cloud data sorted according to Morton coding or Hilbert coding;
FIG. 7 is a schematic diagram illustrating a prediction sequence of even-numbered points in the point cloud data sorted according to Morton coding or Hilbert coding.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the invention provides a bidirectional point cloud attribute prediction compression method, a bidirectional point cloud attribute prediction compression device, bidirectional point cloud attribute prediction compression equipment and bidirectional point cloud attribute prediction compression media, so that the self-adaption determination of the spatial bias coefficient is realized, the point which is the closest in space geometry can be more accurately found, and the loss in the point cloud attribute compression is reduced.
Aiming at the problems in the prior art, the embodiment of the invention provides a bidirectional point cloud attribute prediction compression method, which comprises the following steps:
respectively calculating the standard deviation of the point cloud data on a geometric coordinate axis;
determining a spatial bias coefficient according to the standard deviation;
performing Morton coding sorting or Hilbert coding sorting on the geometric information of the point cloud data, and determining a Morton sequence or a Hilbert sequence;
performing attribute prediction according to the Morton sequence or the Hilbert sequence combined with the sequence number, and determining an attribute predicted value;
and performing attribute prediction compression on the point cloud data according to the attribute prediction value.
Preferably, the respectively calculating the standard deviation of the point cloud data on the geometric coordinate axis includes:
determining three one-dimensional arrays according to the X, Y and Z coordinates of the point cloud data;
determining standard deviations of the point cloud data X, Y and Z according to the one-dimensional array;
the calculation formula of the standard deviation is as follows:
wherein S is standard deviation, n is the number of elements in the one-dimensional array, i is a positive integer, A i Is the ith element in the one-dimensional array, and mu is the mean value of all the elements in the one-dimensional array.
Referring to fig. 2, fig. 2 is a first frame Ford _01_1mm-0100 in a dynamically acquired point cloud data Ford _01_1mm data set, and the frame data Ford _01_1mm-0100 is analyzed on a geometric coordinate to respectively obtain fig. 3, fig. 4, and fig. 5, fig. 3 is a coordinate statistical diagram of the frame data in an X coordinate axis direction, fig. 4 is a coordinate statistical diagram of the frame data in a Y coordinate axis direction, and fig. 5 is a coordinate statistical diagram of the frame data in a Z coordinate axis direction; the degree of dispersion of the frame data in the X-axis direction and the Y-axis direction is far higher than that in the Z-axis direction obtained by analyzing the frame data in combination with the images in FIG. 2, FIG. 3, FIG. 4 and FIG. 5, so that the spatial bias coefficient is adaptively determined based on the degree of dispersion of the point cloud data in the X-axis direction, the Y-axis direction and the Z-axis direction.
The specific implementation process is that firstly, the X, Y and Z coordinates of the point cloud data are respectively extracted to obtain three one-dimensional arrays X ═ X 1 ，x 2 ，x 3 ，…，x n ]，Y＝[y 1 ，y 2 ，y 3 ，…，y n ]，Z＝[z 1 ，z 2 ，z 3 ，…，z n ]Calculating the standard deviation S of the point cloud data X, Y and Z by using a standard deviation calculation formula X ，S Y ，S Z Wherein mu is the mean value of A, namely the mean values of X, Y and Z in the method, and the corresponding standard deviation can be calculated according to the mean values.
Preferably, said determining a spatial bias coefficient according to the standard deviation comprises:
determining the coordinate dispersion degree of the point cloud data according to the standard deviation;
and determining a spatial bias coefficient according to the coordinate discrete degree.
Wherein, according to the standard deviation S X ，S Y ，S Z ，S X As standard deviation on the X coordinate axis, S Y Is the standard deviation on the Y axis, S Z For the standard deviation on the Z axis, respectively calculating S X /S Z ，S Y /S Z Obtaining RatioOfXZ and RatioOfYZ; RatioOfXZ is S X /S Z Value of (1), RatioOfYZ is S Y /S Z A value of (d); the final spatial bias coefficient θ is set according to the magnitudes of ratiooffz and RatioOfYZ.
Preferably, the performing attribute prediction according to the morton sequence or the hilbert sequence combined with the sequence number, and determining an attribute prediction value includes:
according to the Morton sequence or the Hilbert sequence, performing unidirectional attribute prediction on odd points, and determining a first attribute prediction value corresponding to the odd points;
and according to the Morton sequence or the Hilbert sequence, after all odd points are predicted, performing bidirectional attribute prediction on the even points, and determining a second attribute predicted value corresponding to the even points.
And performing attribute prediction on the points with the odd serial numbers in the Morton sequence or the Hilbert sequence, and performing attribute prediction on the even points after all the odd points are predicted.
Preferably, the performing unidirectional attribute prediction on the odd-numbered point according to the morton order or the hilbert order, and the specific step of determining the first attribute prediction value corresponding to the odd-numbered point is:
determining a search range of the unidirectional attribute prediction as an odd point smaller than the odd point;
determining the distance of each adjacent point according to the search range predicted by the unidirectional attribute;
determining a weight value of a reference point according to the search range predicted by the unidirectional attribute, wherein the reference point is at least one point with the minimum distance selected from the adjacent points;
and determining a first attribute predicted value of the odd point according to the search range of the unidirectional attribute prediction.
With reference to fig. 6, fig. 6 is a schematic diagram of a prediction sequence of odd-numbered points in point cloud data sorted according to morton coding or hilbert coding, where 2k-1 and 2k +1 represent odd-numbered points, and 2N +1 represents the last odd-numbered point in the point cloud data, for example, when performing attribute prediction on the point 2k-1, a search range is all odd-numbered points before the point 2 k-1; the method comprises the following specific steps: firstly, carrying out attribute prediction on points with odd-numbered Morton order or Hilbert order, determining a search range for each odd-numbered point, wherein the search range is the odd-numbered point with the Morton order or the Hilbert order smaller than the odd-numbered point, and setting the geometric coordinate of the current point as (x) i ，y i ，z i ) Then, the odd-numbered points, i.e., the near points, in the search range are traversed according to the Morton order or the Hilbert order, and the formula d is used in the search range in combination with the spatial bias coefficient θ as described above j ＝|x i -x ij |+|y i -y ij |+θ*|z i -z ij I, calculating the distance between each adjacent point and the odd points; selecting the distance d from all the traversal points j The smallest K points, i.e., reference points, K is typically 3, and the weight of each reference point is:
let the attribute reconstruction value of each proximity point be
Then calculate attribute predictor A 'for the current point' i The formula is as follows:
preferably, the specific step of performing bidirectional attribute prediction on the even points after all odd points are predicted according to the morton sequence or the hilbert sequence, and determining the second attribute prediction value corresponding to the even points comprises:
determining the search range of the bidirectional attribute prediction as all points smaller than the even number points and odd number points larger than the even number points;
calculating and determining the distance of each adjacent point according to the search range predicted by the bidirectional attribute;
calculating and determining a weight of a reference point according to the search range of the bidirectional attribute prediction, wherein the reference point is at least one point with the minimum distance selected from the adjacent points;
and determining a second attribute predicted value of the odd point according to the search range of the bidirectional attribute prediction.
With reference to fig. 7, fig. 7 is a schematic diagram illustrating a prediction sequence of even-numbered points in the point cloud data sorted according to morton coding or hilbert coding; for example, when the point 2k is subjected to attribute prediction, the search range is all points before the point 2k and all odd points after the point 2 k; the method comprises the following specific steps: after unidirectional attribute prediction is carried out on all points with odd Morton order or Hilbert order, bidirectional attribute prediction is carried out on points with even Morton order or Hilbert order, and the search range of the bidirectional attribute prediction is all points smaller than the even number points and odd number points larger than the even number points; and then, calculating by using the method to obtain a predicted point, a weight value and a predicted value.
Preferably, the performing attribute prediction compression on the point cloud data according to the attribute prediction value includes:
determining an attribute residual error of the point cloud data according to the attribute predicted value;
performing quantization processing according to the attribute residual of the point cloud data, and determining a residual value after quantization;
and performing coding processing according to the quantized residual error value to determine a binary code.
Referring to fig. 1, fig. 1 is a flowchart of a specific algorithm according to an embodiment of the present invention, and the flowchart is as follows: firstly, respectively calculating coordinate standard deviations of the point cloud data on X, Y and Z coordinate axes according to the standard deviation calculation formula, and obtaining a spatial bias coefficient according to the coordinate standard deviations; obtaining a geometric coordinate Morton sequence or a Hilbert sequence of the point cloud data according to the Morton code coding sequence or the Hilbert coding sequence; performing attribute prediction on the Morton sequence or the Hilbert sequence, judging the sequence number of the current point, and performing unidirectional attribute prediction on the odd-sequence point which is in front of the current point according to the search range if the sequence number is the odd sequence; if the current point is an even sequence point, judging whether all the odd sequence points are completely predicted, and if not, skipping the current point; if the odd-sequence points are completely predicted, performing bidirectional attribute prediction according to all the points before the current point and the odd-sequence points after the current point in the search range; and determining a predicted point, a weight and an attribute predicted value according to the attribute prediction, and performing residual prediction, quantization and coding on the point cloud data.
The embodiment of the invention also discloses a bidirectional point cloud attribute prediction compression device, which comprises the following modules:
the first module is used for respectively calculating the standard deviation of the point cloud data on the geometric coordinate axis;
a second module for determining a spatial bias coefficient based on the standard deviation;
the third module is used for conducting Morton coding sequencing or Hilbert coding sequencing on the geometric information of the point cloud data and determining the Morton sequence or the Hilbert sequence;
a fourth module, configured to perform attribute prediction according to the morton sequence or the hilbert sequence combined with the sequence number, and determine an attribute prediction value;
and the fifth module is used for performing attribute prediction compression on the point cloud data according to the attribute prediction value.
The embodiment of the invention also discloses an electronic device, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
The embodiment of the invention also discloses a computer readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to realize the method.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform a method as described above.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise indicated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. A bidirectional point cloud attribute prediction compression method is characterized by comprising the following steps:
respectively calculating the standard deviation of the point cloud data on a geometric coordinate axis;
wherein, the standard deviation of the point cloud data on the geometric coordinate axis is calculated respectively, and the method comprises the following steps:
determining three one-dimensional arrays according to the X, Y and Z coordinates of the point cloud data;
determining standard deviations of the point cloud data X, Y and Z according to the one-dimensional array;
the calculation formula of the standard deviation is as follows:
wherein S is standard deviation, n is the number of elements in the one-dimensional array, i is a positive integer, A i Is the ith element in the one-dimensional array, mu is the average value of all the elements in the one-dimensional array;
determining a spatial bias coefficient according to the standard deviation;
wherein said determining spatial bias coefficients from said standard deviations comprises:
determining the coordinate dispersion degree of the point cloud data according to the standard deviation;
determining a spatial bias coefficient according to the size of the coordinate discrete degree;
performing Morton coding ordering or Hilbert coding ordering on the geometric information of the point cloud data, and determining a Morton order or a Hilbert order, wherein the geometric information of the point cloud data comprises the spatial bias coefficient;
performing attribute prediction according to the Morton sequence or the Hilbert sequence combined with the sequence number, and determining an attribute predicted value;
performing attribute prediction compression on the point cloud data according to the attribute prediction value;
wherein, the attribute prediction is performed according to the Morton order or the Hilbert order combined sequence number, and the determining of the attribute prediction value comprises the following steps:
according to the Morton sequence or the Hilbert sequence, performing unidirectional attribute prediction on odd-numbered points, and determining first attribute predicted values corresponding to the odd-numbered points;
according to the Morton sequence or the Hilbert sequence, after all odd points are predicted, bidirectional attribute prediction is carried out on the even points, and second attribute predicted values corresponding to the even points are determined;
the specific steps of performing unidirectional attribute prediction on the odd-numbered points according to the morton sequence or the Hilbert sequence and determining the first attribute predicted value corresponding to the odd-numbered points are as follows:
determining the search range of the unidirectional attribute prediction as an odd point smaller than the odd point sequence number;
determining the distance of each adjacent point according to the search range of the unidirectional attribute prediction and the spatial bias coefficient;
the calculation formula of the distance is as follows:
d j ＝|x i -x ij |+|y i -y ij |+θ*|z i -z ij |
in the formula (d) j Is distance, θ is the spatial bias coefficient, (x) i ,y i ,z i ) Is the geometric coordinate of an odd number of points, (x) ij ,y ij ,z ij ) Is the geometric coordinate of the adjacent point, j is a positive integer;
determining a weight value of a reference point according to the search range predicted by the unidirectional attribute, wherein the reference point is at least one point with the minimum distance selected from the adjacent points;
the weight value w ij The calculation formula of (c) is:
determining a first attribute predicted value of the odd point according to the search range of the unidirectional attribute prediction;
the first attribute prediction value
The calculation formula of (2) is as follows:
in the formula (I), the compound is shown in the specification,
and (5) reconstructing a value for the attribute of each adjacent point, wherein k is the number of the reference points.
2. The method of claim 1, wherein the bi-directional point cloud attribute prediction compression method comprises the following specific steps of performing bi-directional attribute prediction on even points after all odd points are predicted according to the morton sequence or the hilbert sequence, and determining second attribute predicted values corresponding to the even points:
determining the search range of the bidirectional attribute prediction as all points smaller than the even number points and odd number points larger than the even number points;
calculating and determining the distance of each adjacent point according to the search range predicted by the bidirectional attribute;
calculating and determining a weight of a reference point according to the search range of the bidirectional attribute prediction, wherein the reference point is at least one point with the minimum distance selected from the adjacent points;
and determining a second attribute predicted value of the odd point according to the search range of the bidirectional attribute prediction.
3. The bidirectional point cloud attribute prediction compression method of claim 1, wherein the performing attribute prediction compression on the point cloud data according to the attribute prediction value comprises:
determining an attribute residual error of the point cloud data according to the attribute predicted value;
performing quantization processing according to the attribute residual of the point cloud data, and determining a residual value after quantization;
and performing coding processing according to the quantized residual error value to determine a binary code.
4. A bidirectional point cloud attribute prediction compression device is characterized by comprising:
the first module is used for respectively calculating the standard deviation of the point cloud data on the geometric coordinate axis;
the first module is used for respectively calculating the standard deviation of the point cloud data on a geometric coordinate axis, and comprises the following steps:
determining three one-dimensional arrays according to the X, Y and Z coordinates of the point cloud data;
determining standard deviations of the point cloud data X, Y and Z according to the one-dimensional array;
the calculation formula of the standard deviation is as follows:
wherein S is standard deviation, n is the number of elements in the one-dimensional array, i is a positive integer, A i Is the ith element in the one-dimensional array, mu is the average value of all the elements in the one-dimensional array;
a second module for determining a spatial bias coefficient based on the standard deviation;
wherein the second module is configured to determine a spatial bias coefficient according to the standard deviation, and includes:
determining the coordinate dispersion degree of the point cloud data according to the standard deviation;
determining a spatial bias coefficient according to the size of the coordinate discrete degree;
a third module, configured to perform morton coding ordering or hilbert coding ordering on the geometric information of the point cloud data, and determine a morton order or a hilbert order, where the geometric information of the point cloud data includes the spatial bias coefficient;
the fourth module is used for carrying out attribute prediction according to the Morton sequence or the Hilbert sequence combined sequence number and determining an attribute predicted value;
the fourth module is configured to perform attribute prediction according to the morton sequence or the hilbert sequence combined with the sequence number, and determine an attribute prediction value, where the attribute prediction value includes:
according to the Morton sequence or the Hilbert sequence, performing unidirectional attribute prediction on odd points, and determining a first attribute prediction value corresponding to the odd points;
according to the Morton sequence or the Hilbert sequence, after all odd points are predicted, bidirectional attribute prediction is carried out on the even points, and second attribute predicted values corresponding to the even points are determined;
the specific steps of performing unidirectional attribute prediction on the odd-numbered points according to the morton sequence or the Hilbert sequence and determining the first attribute predicted value corresponding to the odd-numbered points are as follows:
determining the search range of the unidirectional attribute prediction as an odd point smaller than the odd point sequence number;
determining the distance of each adjacent point according to the search range of the unidirectional attribute prediction and the spatial bias coefficient;
the calculation formula of the distance is as follows:
d j ＝|x i -x ij |+|y i -y ij |+θ*|z i -z ij |
in the formula (d) j Is distance, θ is the spatial bias coefficient, (x) i ，y i ，z i ) Is the geometric coordinate of an odd number of points, (x) ij ，y ij ，z ij ) Is the geometric coordinate of the adjacent point, j is a positive integer;
determining a weight value of a reference point according to the search range predicted by the unidirectional attribute, wherein the reference point is at least one point with the minimum distance selected from the adjacent points;
the weight value w ij The calculation formula of (2) is as follows:
determining a first attribute predicted value of the odd point according to the search range of the unidirectional attribute prediction;
the first attribute prediction value
The calculation formula of (2) is as follows:
in the formula (I), the compound is shown in the specification,
reconstructing a value for the attribute of each adjacent point, wherein k is the number of reference points;
and the fifth module is used for performing attribute prediction compression on the point cloud data according to the attribute prediction value.
5. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method according to any one of claims 1-3.
6. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-3.
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