Point cloud quality evaluation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a point cloud quality assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a supervision image corresponding to a to-be-assessed point cloud in response to a received quality assessment instruction for the to-be-assessed point cloud and the to-be-assessed point cloud having a corresponding supervision image; obtaining a target projection image corresponding to the to-be-evaluated point cloud; determining a quality score of the target projection image based on the target projection image and the supervision image; and determining point cloud quality information of the to-be-evaluated point cloud based on the quality score of the target projection image. The target projection image of the to-be-evaluated point cloud and the supervision image of the to-be-evaluated point cloud are acquired, and the point cloud quality of the to-be-evaluated point cloud is determined by using the supervision image and the first target image, so that the point cloud quality of the to-be-evaluated point cloud is efficiently and accurately evaluated, and the point cloud quality evaluation difficulty is reduced.

Description

Technical Field

[0001] The present disclosure relates to point cloud quality assessment technology, and in particular to a point cloud quality assessment method, device, electronic device and storage medium. Background Art

[0002] In recent years, with the rapid development of 3D (3-Dimensional) data acquisition and processing technology, point cloud as a typical 3D data type has also attracted more and more attention, and has played a huge role in the development of many fields such as unmanned driving, mixed reality, surveying and mapping, and medical imaging. Similar to traditional images and videos, point clouds will inevitably introduce various distortions during the acquisition process, affecting the quality of point clouds, and the quality of point clouds is related to the effect of using point clouds. Therefore, a method for efficiently evaluating the quality of point clouds is urgently needed. Summary of the invention

[0003] In order to solve the above problems, the embodiments of the present disclosure provide a point cloud quality assessment method, device, electronic device and storage medium.

[0004] One aspect of an embodiment of the present disclosure provides a point cloud quality assessment method, comprising: in response to receiving a quality assessment instruction for a point cloud to be evaluated, and the point cloud to be evaluated has a corresponding supervisory image, obtaining a supervisory image corresponding to the point cloud to be evaluated; obtaining a target projection image corresponding to the point cloud to be evaluated; determining a quality score of the target projection image based on the target projection image and the supervisory image; and determining point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image.

[0005] Another aspect of the embodiments of the present disclosure provides a point cloud quality assessment device, including: a supervisory image acquisition module, used to obtain a supervisory image corresponding to the point cloud to be evaluated in response to receiving a quality assessment instruction for the point cloud to be evaluated, and the point cloud to be evaluated has a corresponding supervisory image; a projection image determination module, used to obtain a target projection image corresponding to the point cloud to be evaluated; a first scoring module, used to determine a quality score of the target projection image based on the target projection image and the supervisory image; and a first quality assessment module, used to determine point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image.

[0006] Another aspect of the embodiments of the present disclosure provides an electronic device, comprising: a memory for storing a computer program; a processor for executing the computer program stored in the memory, and when the computer program is executed, the above-mentioned point cloud quality assessment method is implemented.

[0007] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned point cloud quality assessment method is implemented.

[0008] In the disclosed embodiment, by acquiring a target projection image of the point cloud to be evaluated and a supervisory image of the point cloud to be evaluated, and using the supervisory image and the first target image to determine the point cloud quality of the point cloud to be evaluated, not only efficient and accurate evaluation of the point cloud quality of the point cloud to be evaluated is achieved, but also the difficulty of point cloud quality evaluation is reduced.

[0009] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0011] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which: Figure 1 is a flow chart of a point cloud quality assessment method provided by an exemplary embodiment of the present disclosure; Figure 2 is a flowchart of step S130 provided by an exemplary embodiment of the present disclosure; Figure 3 is a flow chart of a point cloud quality assessment method provided by another exemplary embodiment of the present disclosure; Figure 4 is a structural schematic diagram of a point cloud quality assessment device provided by an exemplary embodiment of the present disclosure; Figure 5 The figure is a schematic diagram of the structure of an application embodiment of the electronic device disclosed in the present invention. DETAILED DESCRIPTION

[0012] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0013] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0014] It should also be understood that in the embodiments of the present disclosure, “plurality” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0015] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0016] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are in an "or" relationship.

[0017] It should also be understood that the description of the various embodiments in the present disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0018] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0019] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0020] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0021] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] The embodiments of the present disclosure can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0023] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0024] Figure 1 is a flow chart of a point cloud quality assessment method provided by an exemplary embodiment of the present disclosure. This embodiment can be applied in electronic devices, such as Figure 1 As shown, the point cloud quality assessment method of this embodiment may include the following steps: Step S110 , in response to receiving a quality assessment instruction for a point cloud to be assessed, and the point cloud to be assessed has a corresponding supervisory image, obtaining a supervisory image corresponding to the point cloud to be assessed.

[0025] Among them, the supervision image can be used to represent a planar image of the point cloud to be evaluated.

[0026] In one embodiment, a supervision image library may be pre-selected and created, and the supervision image library includes a plurality of supervision images, each of which has an identifier. When the quality assessment instruction includes the identifier of the supervision image, it is determined that the point cloud to be evaluated has the supervision image. Based on the identifier of the supervision image in the quality assessment instruction, the supervision image corresponding to the point cloud to be evaluated may be determined in the supervision image library.

[0027] Step S120, obtaining a target projection image corresponding to the point cloud to be evaluated.

[0028] The target projection image is used to represent the plane image of the point cloud to be evaluated.

[0029] In one embodiment, the point cloud to be evaluated may be projected into a two-dimensional image (plane image), and the two-dimensional image may be determined as a target projection image. For example, the target projection image corresponding to the point cloud to be evaluated may be determined by using a coordinate system conversion method.

[0030] Step S130: determining a quality score of the target projection image based on the target projection image and the supervision image.

[0031] The quality score of the target projection image is used to indicate the quality of the target projection image, and the quality score is proportional to the quality of the first projection image, that is, the higher the quality score, the better the quality of the target projection image.

[0032] In one embodiment, a correspondence between a preset difference value and a score can be pre-set, and a preset image difference algorithm is used to determine the difference value between the target projection image and the supervision image. Then, based on the correspondence between the preset difference value and the score, a score corresponding to the difference value between the target projection image and the supervision image is determined, and the score is determined as the quality score of the target projection image, wherein the preset image difference algorithm can be, for example, a mean square error (MSE) or a peak signal-to-noise ratio (PSNR), etc.

[0033] Step S140: determining point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image.

[0034] The point cloud quality information is used to represent the point cloud quality of the point cloud to be evaluated. The point cloud quality information of the point cloud to be evaluated is proportional to the quality score of the target projection image, that is, the higher the quality score of the target projection image, the better the quality of the evaluation point cloud.

[0035] In the disclosed embodiment, by acquiring a target projection image of the point cloud to be evaluated and a supervisory image of the point cloud to be evaluated, and using the supervisory image and the first target image to determine the point cloud quality of the point cloud to be evaluated, not only efficient and accurate evaluation of the point cloud quality of the point cloud to be evaluated is achieved, but also the difficulty of point cloud quality evaluation is reduced.

[0036] In some optional implementations, step S120 in the implementation of the present disclosure may include the following steps: based on a preset projection method, obtaining target projection images corresponding to the point cloud to be evaluated at multiple preset angles.

[0037] There may be multiple preset angles, and there may be multiple corresponding first target projection images. The preset projection method may be a 3D Gaussian Splatting method.

[0038] Exemplarily, the size of the point cloud to be evaluated can be initialized using a K-NearestNeighbor (KNN) algorithm, and then a 3D Gaussian ellipsoid set corresponding to the point cloud to be evaluated is created using spherical harmonics. Then, a polar coordinate is established, and 360° is divided into n equal parts in the polar coordinate, so that n2 angles (preset angles) are obtained in space. Then, the 3D Gaussian splash method is used to determine the image projected at each angle (preset angle) one by one, and the image projected at each angle (preset angle) is determined as a target projection image.

[0039] Figure 2 is a flow chart of step S130 provided by an exemplary embodiment of the present disclosure. In an optional implementation, Figure 2As shown, step S130 may include the following steps: Step S131, determining a first image quality score based on the difference between the target projection image and the supervision image.

[0040] In one embodiment, the difference between the target projection image and the supervision image corresponding to each preset angle can be calculated one by one to determine the minimum difference value, and the score corresponding to the minimum difference value can be determined using the correspondence between the preset difference value and the score, and the score can be used to determine the first image quality score.

[0041] Step S132: determining a second image quality score based on the image quality information of the target projection image.

[0042] The image quality information includes image quality data of the first projection image. For example, the image quality information may include the mean and standard deviation of the first projection image. The mean refers to the average value of image pixels, which reflects the average brightness of the image, and the standard deviation refers to the degree of dispersion of the grayscale values ​​of the image pixels relative to the mean.

[0043] In one embodiment, a correspondence between preset image quality information and a score may be created in advance, for example, a correspondence between a preset mean (image quality information) and a score may be created. Based on the image quality information of the target projection image, the second image quality score may be determined using the correspondence between the preset image quality information and the score.

[0044] Step S133: determining a quality score of the target projection image based on the first image quality score and the second image quality score.

[0045] The average value of the first image quality score and the second image quality score may be used as the quality score of the target projection image.

[0046] In the embodiment of the present disclosure, by determining the target projection image corresponding to the point cloud to be evaluated at a preset angle, the quality of the point cloud to be evaluated can be evaluated from different angles, thereby improving the accuracy of the evaluation of the point cloud to be evaluated.

[0047] In an optional embodiment, step S133 in the embodiment of the present disclosure may include: obtaining weights corresponding to the first image quality score and the second image quality score, respectively, and then determining the score information of the target projection image based on the first image quality score, the second image quality score, and the weights corresponding to the first image quality score and the second image quality score, respectively.

[0048] In one embodiment, a correspondence between preset scoring types and weights can be created in advance, and the scoring type can be the type of the method for generating an image quality score. For example, the scoring type of the method for generating a first image quality score can be defined in advance as a first type, and the scoring type of the method for generating a second image quality score can be defined as a second type. The weights corresponding to the first type and the weights corresponding to the second type are defined, and the weights corresponding to the first type and the second type constitute the correspondence between the preset scoring types and the weights. Based on the correspondence between the preset scoring types and the weights, the weights corresponding to the first image quality score and the second image quality score, respectively, are determined, and then the sum of the value obtained by multiplying the first image quality score by its weight and the value obtained by multiplying the second image quality score by its weight is determined as the quality score of the target projection image.

[0049] Figure 3 is a flow chart of a point cloud quality assessment method provided by another exemplary embodiment of the present disclosure. In some optional implementations, such as Figure 3 As shown, the point cloud quality assessment method also includes the following steps: Step S210 , in response to receiving a quality assessment instruction for a point cloud to be assessed, and the point cloud to be assessed does not have a corresponding supervisory image, determining a second image quality score based on image quality information of a target projection image.

[0050] When the quality assessment instruction does not include an identification of a supervisory image, it is determined that the point cloud to be assessed does not have a supervisory image.

[0051] Exemplarily, the image quality information may include sharpness, and the sharpness of the target projection images corresponding to each preset angle can be determined by a convolutional neural network (CNN), and the sharpness of each target projection image with the highest sharpness is selected as the target sharpness. Then, based on the correspondence between the preset score and the sharpness, a first sub-score corresponding to the target sharpness is determined. The scores of a preset number (100) of people on each target projection image can be collected, and then the average of the scores of the preset number of people on each target projection image is determined as the second sub-score, and the average of the first sub-score and the second sub-score is determined as the second image quality score.

[0052] Step S220: determining an auxiliary quality score based on the point cloud density of the point cloud to be evaluated.

[0053] Among them, based on the point cloud density (Point Cloud Density) of the point cloud to be evaluated, the score corresponding to the point cloud density of the point cloud to be evaluated can be determined by using the relationship between the preset point cloud density and the score, and the score can be determined as the auxiliary quality score.

[0054] Step S230: determining a quality score of the target projection image based on the second image quality score and the auxiliary quality score.

[0055] The average value of the second image quality score and the auxiliary quality score may be determined as the quality score of the target projection image.

[0056] Step S240: determining point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image.

[0057] In some optional embodiments, step S230 in the embodiment of the present disclosure may include: obtaining the weights corresponding to the second image quality score and the auxiliary quality score, and then determining the quality score of the target projection image based on the second image quality score and the auxiliary quality score, and the weights corresponding to the second image quality score and the auxiliary quality score, respectively.

[0058] In one embodiment, the correspondence between the preset scoring type and the weight also includes the weight corresponding to the third type. The scoring type of the method for generating the auxiliary quality score can be defined as the third type in advance, and the weight corresponding to the third type is defined.

[0059] Based on the correspondence between the preset scoring type and the weight, the weight corresponding to the second image quality score and the weight corresponding to the auxiliary quality score can be determined, and then the value obtained by multiplying the second image quality score by its weight and the sum of the value obtained by multiplying the auxiliary quality score by its weight are determined as the quality score of the target projection image.

[0060] Figure 4 Schematic diagram of the structure of a point cloud quality assessment device provided by an exemplary embodiment of the present disclosure. Figure 4 As shown, the point cloud quality assessment device may include: The supervision image acquisition module 310 is used for, in response to receiving a quality assessment instruction for a point cloud to be evaluated, and the point cloud to be evaluated has a corresponding supervision image, acquiring a supervision image corresponding to the point cloud to be evaluated; A projection image determination module 320 is used to obtain a target projection image corresponding to the point cloud to be evaluated; A first scoring module 330, configured to determine a quality score of the target projection image based on the target projection image and the supervision image; The first quality assessment module 340 is configured to determine point cloud quality information of the point cloud to be assessed based on the quality score of the target projection image.

[0061] In some possible implementations of the present disclosure, the projection image determination module 320 in the embodiment of the present disclosure is specifically configured to obtain target projection images corresponding to the point cloud to be evaluated at multiple preset angles based on a preset projection method.

[0062] In some possible implementations of the present disclosure, the first scoring module 330 in the embodiment of the present disclosure is specifically used to determine a first image quality score based on the difference between the target projection image and the supervision image; determine a second image quality score based on the image quality information of the target projection image; and determine a quality score of the target projection image based on the first image quality score and the second image quality score.

[0063] In some possible implementations of the present disclosure, the determining of the quality score of the target projection image based on the first image quality score and the second image quality score in the embodiment of the present disclosure is further used to: Obtaining weights corresponding to the first image quality score and the second image quality score respectively; Based on the first image quality score, the second image quality score, and weights corresponding to the first image quality score and the second image quality score, respectively, score information of the target projection image is determined.

[0064] In some possible implementations of the present disclosure, the point cloud quality assessment device in the embodiment of the present disclosure further includes: A second scoring module is configured to determine a second image quality score based on the image quality information of the target projection image in response to receiving a quality assessment instruction for the point cloud to be assessed, and the point cloud to be assessed does not have a corresponding supervision image; A third scoring module, configured to determine an auxiliary quality score based on the point cloud density of the point cloud to be evaluated; a fourth scoring module, configured to determine a quality score of the target projection image based on the second image quality score and the auxiliary quality score; The second quality assessment module is used to determine the point cloud quality information of the point cloud to be assessed based on the quality score of the target projection image.

[0065] In some possible implementations of the present disclosure, the fourth scoring module in the embodiment of the present disclosure is specifically used to obtain the weights corresponding to the second image quality score and the auxiliary quality score, respectively; based on the second image quality score and the auxiliary quality score, and the weights corresponding to the second image quality score and the auxiliary quality score, respectively, determine the quality score of the target projection image.

[0066] The point cloud quality assessment device of the embodiment of the present disclosure corresponds to the embodiment of the point cloud quality assessment method of the present disclosure, and the relevant contents can be referenced to each other and will not be repeated here.

[0067] The beneficial technical effects corresponding to the exemplary embodiments of the point cloud quality assessment device of the disclosed embodiments can be found in the corresponding beneficial technical effects of the above-mentioned corresponding exemplary method part, which will not be repeated here.

[0068] In addition, an embodiment of the present disclosure further provides an electronic device, including: Memory for storing computer programs; The processor is used to execute the computer program stored in the memory, and when the computer program is executed, the point cloud quality assessment method described in any of the above embodiments of the present disclosure is implemented.

[0069] Figure 5 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed in the present invention. Figure 5 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signals from them.

[0070] like Figure 5 As shown, the electronic device includes one or more processors and memory.

[0071] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0072] The memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the point cloud quality assessment method of each embodiment of the present disclosure described above and / or other desired functions.

[0073] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0074] In addition, the input device may also include, for example, a keyboard, a mouse, and the like.

[0075] The output device can output various information to the outside, including the determined distance information, direction information, etc. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0076] Of course, to simplify, Figure 5 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0077] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the point cloud quality assessment method according to various embodiments of the present disclosure described in the above part of this specification.

[0078] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0079] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the point cloud quality assessment method according to various embodiments of the present disclosure described in the above part of this specification.

[0080] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0081] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0082] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0083] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0084] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0085] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0086] It should also be noted that in the apparatus, device and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0087] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0088] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A point cloud quality assessment method, characterized in that: include: In response to receiving a quality assessment instruction for a point cloud to be assessed, and the point cloud to be assessed has a corresponding supervisory image, acquiring a supervisory image corresponding to the point cloud to be assessed; Acquire a target projection image corresponding to the point cloud to be evaluated; Determining a quality score of the target projection image based on the target projection image and the supervision image; Based on the quality score of the target projection image, point cloud quality information of the point cloud to be evaluated is determined.

2. The method according to claim 1, characterized in that The obtaining of the target projection image corresponding to the point cloud to be evaluated includes: Based on a preset projection method, target projection images corresponding to the point cloud to be evaluated at multiple preset angles are obtained.

3. The method according to claim 1, characterized in that: The step of determining a quality score of the target projection image based on the target projection image and the supervision image includes: determining a first image quality score based on a difference between the target projection image and the supervision image; determining a second image quality score based on the image quality information of the target projection image; A quality score of the target projection image is determined based on the first image quality score and the second image quality score.

4. The method according to claim 3, characterized in that The determining, based on the first image quality score and the second image quality score, of the quality score of the target projection image comprises: Obtaining weights corresponding to the first image quality score and the second image quality score respectively; Based on the first image quality score, the second image quality score, and weights corresponding to the first image quality score and the second image quality score, respectively, score information of the target projection image is determined.

5. The method according to claim 3, characterized in that: Also includes: In response to receiving a quality assessment instruction for the point cloud to be assessed, and the point cloud to be assessed does not have a corresponding supervision image, determining a second image quality score based on image quality information of the target projection image; Determining an auxiliary quality score based on the point cloud density of the point cloud to be evaluated; determining a quality score of the target projection image based on the second image quality score and the auxiliary quality score; Based on the quality score of the target projection image, point cloud quality information of the point cloud to be evaluated is determined.

6. The method according to claim 5, characterized in that The step of determining the quality score of the target projection image based on the second image quality score and the auxiliary quality score comprises: Obtaining weights corresponding to the second image quality score and the auxiliary quality score respectively; The quality score of the target projection image is determined based on the second image quality score and the auxiliary quality score, and the weights corresponding to the second image quality score and the auxiliary quality score respectively.

7. A point cloud quality assessment device, characterized in that: include: A supervision image acquisition module, configured to, in response to receiving a quality assessment instruction for a point cloud to be assessed, and the point cloud to be assessed has a corresponding supervision image, acquire a supervision image corresponding to the point cloud to be assessed; A projection image determination module, used to obtain a target projection image corresponding to the point cloud to be evaluated; A first scoring module, configured to determine a quality score of the target projection image based on the target projection image and the supervision image; The first quality assessment module is used to determine the point cloud quality information of the point cloud to be assessed based on the quality score of the target projection image.

8. The device according to claim 7, characterized in that The projection image determination module is specifically configured to obtain target projection images corresponding to the point cloud to be evaluated at multiple preset angles based on a preset projection method.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to execute the computer program stored in the memory, and when the computer program is executed, the point cloud quality assessment method described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the point cloud quality assessment method described in any one of claims 1 to 6 is implemented.

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