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

Abstract

Embodiments of the present disclosure disclose a point cloud quality evaluation method and device, electronic equipment and a storage medium. The method comprises: in response to receiving a quality evaluation instruction for a point cloud to be evaluated, and the point cloud to be evaluated having a corresponding supervision image, obtaining the supervision 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 supervision image; and determining point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image. Thus, by obtaining the target projection image of the point cloud to be evaluated and the supervision image of the point cloud to be evaluated, and using the supervision image and the first target image to determine the point cloud quality of the point cloud to be evaluated, the point cloud quality of the point cloud to be evaluated is efficiently and accurately evaluated, and the difficulty of point cloud quality evaluation is reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a point cloud quality evaluation technology, in particular to a point cloud quality evaluation method and device, an electronic device and a storage medium. BACKGROUND

[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 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 acquisition, affecting the quality of point cloud data, and the quality of point cloud data is related to the effect of using point cloud data. Therefore, an efficient method for evaluating the quality of point cloud data is urgently needed. SUMMARY

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

[0004] In one aspect of the embodiments of the present disclosure, a point cloud quality evaluation method is provided, comprising: in response to receiving a quality evaluation instruction for a to-be-evaluated point cloud, and the to-be-evaluated point cloud having a corresponding supervision image, acquiring the supervision image corresponding to the to-be-evaluated point cloud; acquiring 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.

[0005] In another aspect of the embodiments of the present disclosure, a point cloud quality evaluation device is provided, comprising: a supervision image acquisition module, configured to, in response to receiving a quality evaluation instruction for a to-be-evaluated point cloud, and the to-be-evaluated point cloud having a corresponding supervision image, acquire the supervision image corresponding to the to-be-evaluated point cloud; a projection image determination module, configured to acquire a target projection image corresponding to the to-be-evaluated point cloud; a first score module, configured to determine a quality score of the target projection image based on the target projection image and the supervision image; and a first quality evaluation module, configured to determine point cloud quality information of the to-be-evaluated point cloud based on the quality score of the target projection image.

[0006] In still another aspect of the embodiments of the present disclosure, an electronic device is provided, comprising: a memory configured to store a computer program; and a processor configured to execute the computer program stored in the memory, and when the computer program is executed, the point cloud quality evaluation method described above is implemented.

[0007] In still another aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, and the computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the point cloud quality evaluation method.

[0008] In the embodiments of the present disclosure, the target projection image of the point cloud to be evaluated and the supervision image of the point cloud to be evaluated are obtained, and the point cloud quality of the point cloud to be evaluated is determined by using the supervision image and the first target image. Thus, the point cloud quality of the point cloud to be evaluated is efficiently and accurately evaluated, and the difficulty of point cloud quality evaluation is reduced.

[0009] The technical solutions of the present disclosure are described in further detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

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

[0011] The present disclosure can be more clearly understood with reference to the following detailed description in conjunction with the accompanying drawings, in which:

[0012] Figure 1 is a flowchart of a point cloud quality evaluation method provided by an exemplary embodiment of the present disclosure;

[0013] Figure 2 is a flowchart of step S130 provided by an exemplary embodiment of the present disclosure;

[0014] Figure 3 is a flowchart of a point cloud quality evaluation method provided by another exemplary embodiment of the present disclosure;

[0015] Figure 4 is a structural schematic diagram of a point cloud quality evaluation device provided by an exemplary embodiment of the present disclosure;

[0016] Figure 5 is a structural schematic diagram of an electronic device according to an application embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of the components and steps set forth in these embodiments are not limiting to the scope of the present disclosure unless otherwise specifically stated.

[0018] Those skilled in the art can understand that the terms "first", "second", and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they represent a necessary logical sequence between them.

[0019] It should also be understood that, in the embodiments of the present disclosure, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0020] It should also be understood that, for any component, data or structure mentioned in the embodiments of the present disclosure, one or more can be generally understood without explicit limitation or in the context of the opposite implication.

[0021] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the front and rear associated objects.

[0022] It should also be understood that the description of the embodiments of the present disclosure emphasizes the differences between the embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, will not be repeated.

[0023] At the same time, it should be understood that, for the convenience of description, the size of each part shown in the drawings is not drawn according to the actual proportion relationship.

[0024] The following description of at least one example embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application or uses.

[0025] The techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but in appropriate cases, the techniques, methods, and devices should be considered as part of the specification.

[0026] It should be noted that: similar signs and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in the subsequent drawings.

[0027] The embodiments of the present disclosure can be applied to terminal devices, computer systems, servers and other electronic devices, 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 terminal devices, computer systems, servers and other electronic devices 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.

[0028] Electronic devices such as terminal devices, computer systems, servers, and the like can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like that perform particular tasks or implement particular abstract data types. Computer systems / servers can be practiced in distributed cloud-computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud-computing environment, program modules can be located in local or remote computer system storage media including storage devices.

[0029] Figure 1 FIG. 1 is a flowchart of a point cloud quality evaluation method according to an example embodiment of the present disclosure. The example embodiment can be applied in an electronic device, such as a terminal device, a computer system, a server, and the like. Figure 1 As shown in FIG. 1, the point cloud quality evaluation method can include the following steps:

[0030] At step S110, in response to receiving a quality evaluation instruction for a point cloud to be evaluated, and the point cloud to be evaluated having a corresponding supervision image, a supervision image corresponding to the point cloud to be evaluated is obtained.

[0031] The supervision image can be used to represent a planar image of the point cloud to be evaluated.

[0032] In an embodiment, a supervision image library can be preselected and created, and the supervision image library includes a plurality of supervision images, each supervision image having an identifier. When the identifier of the supervision image is included in the quality evaluation instruction, it is determined that the point cloud to be evaluated has the supervision image. The identifier of the supervision image in the quality evaluation instruction can be used to determine the supervision image corresponding to the point cloud to be evaluated in the supervision image library.

[0033] At step S120, a target projection image corresponding to the point cloud to be evaluated is obtained.

[0034] The target projection image is used to represent a planar image of the point cloud to be evaluated.

[0035] In an embodiment, the point cloud to be evaluated can be projected into a two-dimensional image (planar image), and the two-dimensional image is determined as the target projection image. For example, the target projection image corresponding to the point cloud to be evaluated can be determined by using a coordinate system conversion method.

[0036] At step S130, a quality score of the target projection image is determined based on the target projection image and the supervision image.

[0037] The quality score of the target projection image is used to represent the quality of the target projection image, and the quality score is directly 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.

[0038] In one embodiment, a preset correspondence between the preset difference value and the score can be set in advance, a difference value between the target projection image and the supervised image is determined by using a preset image difference algorithm, and then based on the preset correspondence between the preset difference value and the score, a score corresponding to the difference value between the target projection image and the supervised 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, Mean-Square Error (MSE) or Peak-Signal to Noise Ratio (PSNR).

[0039] In step S140, the point cloud quality information of the point cloud to be evaluated is determined based on the quality score of the target projection image.

[0040] 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 directly 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 evaluated point cloud.

[0041] In the embodiments of the present disclosure, by obtaining the target projection image of the point cloud to be evaluated and the supervised image of the point cloud to be evaluated, and determining the point cloud quality of the point cloud to be evaluated by using the supervised image and the first target image, the efficient and accurate evaluation of the point cloud quality of the point cloud to be evaluated is realized, and the difficulty of point cloud quality evaluation is reduced.

[0042] In some optional embodiments, step S120 in the embodiments of the present disclosure can include the following steps: based on a preset projection method, obtaining target projection images corresponding to the point cloud to be evaluated at a plurality of preset angles.

[0043] The preset angles can be a plurality of angles, and the corresponding first target projection images can also be a plurality of images. The preset projection method can be a 3D Gaussian Splatting method.

[0044] For example, the size of the point cloud to be evaluated can be initialized by using a K-Nearest Neighbor (KNN) algorithm, then a 3D Gaussian ellipsoid set corresponding to the point cloud to be evaluated is created by using a spherical harmonic function, then a polar coordinate is established, and 360° is divided into n parts in the polar coordinate, so that n2 angles (preset angles) are obtained in space, and then the images projected at each angle (preset angle) are determined one by one by using the 3D Gaussian Splatting method, and the images projected at each angle (preset angle) are determined as a target projection image.

[0045] Figure 2 FIG. 1 is a flowchart of step S130 provided by an exemplary embodiment of the present disclosure. In one optional embodiment, as shown in FIG. 1, step S130 can include the following steps:Figure 2 As shown, step S130 can include the following steps:

[0046] Step S131, determining a first image quality score based on the difference between the target projection image and the supervised image.

[0047] In one embodiment, the difference between the target projection image and the supervised image corresponding to each preset angle can be calculated one by one, the minimum difference value is determined, the score corresponding to the minimum difference value is determined by using the correspondence between the preset difference value and the score, and the score is determined as the first image quality score.

[0048] Step S132, determining a second image quality score based on the image quality information of the target projection image.

[0049] The image quality information includes the image quality data of the first projection image. For example, the image quality information can include the mean and the standard deviation of the first projection image. The mean refers to the average value of the image pixels, which reflects the average brightness of the image, and the standard deviation refers to the dispersion degree of the image pixel grayscale value relative to the mean.

[0050] In one embodiment, a correspondence between the preset image quality information and the score can be created in advance, for example, a correspondence between the preset mean (image quality information) and the score can be created. The second image quality score can be determined based on the image quality information of the target projection image by using the correspondence between the preset image quality information and the score.

[0051] Step S133, determining the quality score of the target projection image based on the first image quality score and the second image quality score.

[0052] The average value of the first image quality score and the second image quality score can be taken as the quality score of the target projection image.

[0053] In the embodiments of the present disclosure, by determining the target projection image corresponding to the preset angle of the to-be-evaluated point cloud, the quality of the to-be-evaluated point cloud is evaluated from different angles, and the accuracy of the evaluation of the to-be-evaluated point cloud is improved.

[0054] In an optional embodiment, step S133 in the embodiments of the present disclosure can include: obtaining the weights corresponding to the first image quality score and the second image quality score, 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.

[0055] In an embodiment, a preset correspondence between a preset score type and a weight can be created in advance, the score type can be a type of a method for generating an image quality score, for example, a score type of a method for generating a first image quality score can be defined as a first type, a score type of a method for generating a second image quality score can be defined as a second type, a weight corresponding to the first type and a weight corresponding to the second type are defined, the weight corresponding to the first type and the weight corresponding to the second type constitute the preset correspondence between the preset score type and the weight, based on the preset correspondence between the preset score type and the weight, weights corresponding to the first image quality score and the second image quality score are determined, then a sum of a value obtained by multiplying the first image quality score by the weight thereof and a value obtained by multiplying the second image quality score by the weight thereof is determined as a quality score of the target projection image.

[0056] Figure 3 is a flowchart of a point cloud quality evaluation method provided by another exemplary embodiment of the present disclosure. In some optional embodiments, as shown in Figure 3 the point cloud quality evaluation method further includes the following steps:

[0057] In step S210, in response to receiving a quality evaluation instruction for a point cloud to be evaluated, and the point cloud to be evaluated does not have a corresponding supervised image, a second image quality score is determined based on image quality information of a target projection image.

[0058] In the quality evaluation instruction, the identification of the supervised image is not included, it is determined that the point cloud to be evaluated does not have the supervised image.

[0059] Exemplarily, the image quality information can include sharpness, the sharpness of the target projection image corresponding to each preset angle can be determined by a convolutional neural network (CNN), the highest sharpness in the sharpness of each target projection image is selected as a target sharpness, then based on a preset correspondence between a preset score and the sharpness, a first sub-score corresponding to the target sharpness is determined, a preset number (100) of people can be collected to score each target projection image, then an average value of the scores of each target projection image by the preset number of people is determined as a second sub-score, and an average value of the first sub-score and the second sub-score is determined as the second image quality score.

[0060] In step S220, an auxiliary quality score is determined based on a point cloud density of the point cloud to be evaluated.

[0061] In the point cloud density (Point Cloud Density) of the point cloud to be evaluated, a preset correspondence between a preset point cloud density and a score is used to determine a score corresponding to the point cloud density of the point cloud to be evaluated, and the score is determined as the auxiliary quality score.

[0062] Step S230, determining the quality score of the target projection image based on the second image quality score and the auxiliary quality score.

[0063] In some optional embodiments, the step S230 in the embodiments of the present disclosure can 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.

[0064] Step S240, determining the point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image.

[0065] In some optional embodiments, the step S230 in the embodiments of the present disclosure can 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.

[0066] In one embodiment, the correspondence between the preset score type and the weight further includes a weight corresponding to a third type. Wherein, the score type of the method for generating the auxiliary quality score can be defined as the third type, and the weight corresponding to the third type is defined.

[0067] The weight corresponding to the second image quality score and the weight corresponding to the auxiliary quality score can be determined based on the correspondence between the preset score type and the weight, and then the sum of the value obtained by multiplying the second image quality score by its weight and the value obtained by multiplying the auxiliary quality score by its weight is determined as the quality score of the target projection image.

[0068] Figure 4 FIG. 1 is a structural schematic diagram of a point cloud quality evaluation device provided by an exemplary embodiment of the present disclosure. As shown in the figure, the point cloud quality evaluation device can include: Figure 4

[0069] The supervision image acquisition module 310 is configured to, in response to receiving a quality evaluation instruction for a point cloud to be evaluated, and the point cloud to be evaluated having a corresponding supervision image, acquire the supervision image corresponding to the point cloud to be evaluated.

[0070] The projection image determination module 320 is configured to acquire a target projection image corresponding to the point cloud to be evaluated.

[0071] The first scoring module 330 is configured to determine a quality score of the target projection image based on the target projection image and the supervision image.

[0072] The first quality evaluation module 340 is configured to determine point cloud quality information of the point cloud to be evaluated based on the quality score of the target projection image.

[0073] ​In some possible implementation manners of the present disclosure, the projection image determination module 320 in the present embodiment is specifically configured to obtain target projection images corresponding to the to-be-evaluated point cloud at a plurality of preset angles based on a preset projection method.

[0074] In some possible implementation manners of the present disclosure, the first scoring module 330 in the present embodiment is specifically configured to determine a first image quality score based on a difference between the target projection image and the supervised image, determine a second image quality score based on 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.

[0075] In some possible implementation manners of the present disclosure, the determination of the quality score of the target projection image based on the first image quality score and the second image quality score in the present embodiment is further configured to:

[0076] obtain weights corresponding to the first image quality score and the second image quality score respectively;

[0077] determine 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.

[0078] In some possible implementation manners of the present disclosure, the point cloud quality evaluation apparatus in the present embodiment further includes:

[0079] a second scoring module configured to, in response to receiving a quality evaluation instruction for the to-be-evaluated point cloud and the to-be-evaluated point cloud not having a corresponding supervised image, determine a second image quality score based on image quality information of the target projection image;

[0080] a third scoring module configured to determine an auxiliary quality score based on a point cloud density of the to-be-evaluated point cloud;

[0081] 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;

[0082] a second quality evaluation module configured to determine point cloud quality information of the to-be-evaluated point cloud based on the quality score of the target projection image.

[0083] In some possible implementation manners of the present disclosure, the fourth scoring module in the embodiments of the present disclosure is specifically configured to obtain weights corresponding to the second image quality score and the auxiliary quality score respectively; and determine 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.

[0084] The point cloud quality evaluation device in the embodiments of the present disclosure corresponds to the embodiments of the point cloud quality evaluation method of the present disclosure, and the related content can be mutually referred to, which will not be described here again.

[0085] The beneficial technical effects of the exemplary embodiments of the point cloud quality evaluation device in the embodiments of the present disclosure can be referred to the corresponding beneficial technical effects of the exemplary method part described above, which will not be described here again.

[0086] In addition, the embodiments of the present disclosure also provide an electronic device, comprising:

[0087] a memory configured to store a computer program;

[0088] a processor configured to execute the computer program stored in the memory, and when the computer program is executed, the point cloud quality evaluation method in any of the embodiments of the present disclosure is implemented.

[0089] Figure 5 The structure schematic diagram of an application embodiment of the electronic device of the present disclosure is shown in the following figure. Figure 5 The electronic device according to the embodiments of the present disclosure is described below with reference to the figure. The electronic device can be any one or both of the first device and the second device, or a single device independent of them, which can communicate with the first device and the second device to receive the collected input signals therefrom.

[0090] As shown in Figure 5 the electronic device includes one or more processors and a memory.

[0091] The processor can be a central processing unit (CPU) or other forms of processing units having data processing capability and / or instruction execution capability, and can control other components in the electronic device to perform desired functions.

[0092] The memory can include one or more computer program products that can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory, for example, can include random access memory (RAM), and / or a cache, etc. The non-volatile memory, for example, can include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage media, and the processor can execute the program instructions to implement the point cloud quality evaluation method of various embodiments of the present disclosure described above and / or other desired functions.

[0093] In one example, the electronic device can further include an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanism (not shown).

[0094] In addition, the input device can further include, for example, a keyboard, a mouse, etc.

[0095] The output device can output various information, including the determined distance information, direction information, etc., to the outside. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0096] Of course, in order to simplify, Figure 5 Only some of the components related to the present disclosure among the electronic device are shown in the middle, and components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device can further include any other appropriate components according to the specific application.

[0097] In addition to the above-described method and device, embodiments of the present disclosure can also be a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform the steps of the point cloud quality evaluation method according to various embodiments of the present disclosure described in the above part of the specification.

[0098] The computer program product can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, etc., and a conventional procedural programming language such as "C" language or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, as a separate software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0099] In addition, an embodiment of the present disclosure can also be a computer readable storage medium, having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the point cloud quality evaluation method according to various embodiments of the present disclosure described in the foregoing parts of the specification.

[0100] The computer readable storage medium can take the form of one or more combinations of any type of readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0101] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction-related hardware, and the aforementioned program can be stored in a computer readable storage medium, and the program executes the steps of the above-mentioned method embodiments when executed; and the aforementioned storage medium includes ROM, RAM, magnetic disc or optical disc, and various storage medium that can store program codes.

[0102] The above describes the basic principles of the present disclosure in combination with specific embodiments, but it should be noted that the advantages, advantages, effects, etc. mentioned in the present disclosure are only examples and are not limiting, and these advantages, advantages, effects, etc. cannot be considered as the various embodiments of the present disclosure must have. In addition, the above specific details are only for the purpose of example and for the purpose of understanding, and the above details do not limit the present disclosure to the above specific details.

[0103] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For system embodiments, since they basically correspond to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0104] The block diagrams of devices, apparatuses, equipment, systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connection, arrangement, configuration must be as shown in the block diagrams. These devices, apparatuses, equipment, systems can be connected, arranged, configured in any manner as will be appreciated by those skilled in the art. Words such as "include," "contain," "have," and the like are open-ended words that are to be interpreted to mean "including but not limited to," and are not to be interpreted as limiting the described embodiment to features, elements, and / or steps disclosed herein. The words "or" and "and" as used herein are to be interpreted as the word "and / or," and are not to be interpreted as requiring both features, elements, and / or steps disclosed herein. The word "such as" as used herein is to be interpreted as the phrase "such as but not limited to," and is not to be interpreted as limiting the described embodiment to features, elements, and / or steps disclosed herein.

[0105] The methods and apparatuses of this disclosure can be implemented in a number of ways. For example, the methods and apparatuses of this disclosure can be implemented using software, hardware, firmware, or any combination of these methods. The above described order of steps for the methods is merely illustrative, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the disclosure can also be implemented as a program recorded in a recording medium, which includes machine readable instructions for implementing the methods according to the disclosure. Thus, the disclosure also covers a recording medium storing a program for executing the methods according to the disclosure.

[0106] It is also important to note that the devices, equipment, and methods of this disclosure can be embodied in a variety of ways. These variations are contemplated as being within the scope of the present disclosure.

[0107] The above description of the disclosed aspects is given for illustrative purposes and is not intended to limit the scope of the disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0108] The above description has been given for illustrative and descriptive purposes. In addition, this description is not intended to limit the embodiments of the disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those of skill 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 evaluated, and the point cloud to be evaluated has a corresponding supervisory image, the supervisory image corresponding to the point cloud to be evaluated is obtained. Obtain the target projection image corresponding to the point cloud to be evaluated; Based on the target projection image and the supervision image, a quality score for the target projection image is determined; Based on the quality score of the target projection image, the point cloud quality information of the point cloud to be evaluated is determined; In response to receiving a quality assessment instruction for the point cloud to be evaluated, and the point cloud to be evaluated does not have a corresponding supervisory image, a second image quality score is determined based on the image quality information of the target projection image. Based on the point cloud density of the point cloud to be evaluated, an auxiliary quality score is determined; The quality score of the target projection image is determined based on the second image quality score and the auxiliary quality score. Based on the quality score of the target projection image, the point cloud quality information of the point cloud to be evaluated is determined; The step of determining the quality score of the target projection image based on the target projection image and the supervised image includes: determining a first image quality score based on the difference between the target projection image and the supervised image; determining a second image quality score based on the image quality information of the target projection image; obtaining the weights corresponding to the first image quality score and the second image quality score respectively; and 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.

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

3. The method according to claim 1, characterized in that, Determining the quality score of the target projection image based on the second image quality score and the auxiliary quality score includes: Obtain the 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, as well as the weights corresponding to the second image quality score and the auxiliary quality score.

4. A point cloud quality assessment device, characterized in that, include: The supervised image acquisition module is used to acquire the supervised 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 supervised image. The projection image determination module is used to obtain the target projection image corresponding to the point cloud to be evaluated; The first scoring module is used to determine the 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. The second scoring module is used to respond to receiving a quality assessment instruction for the point cloud to be evaluated, and the point cloud to be evaluated does not have a corresponding supervisory image, and to determine a second image quality score based on the image quality information of the target projection image. The third scoring module is used to determine an auxiliary quality score based on the point cloud density of the point cloud to be evaluated. The fourth scoring module is used to determine the 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. The first scoring module is specifically used to determine a first image quality score based on the difference between the target projection image and the supervision image; and to determine a second image quality score based on the image quality information of the target projection image. Obtain the weights corresponding to the first image quality score and the second image quality score respectively; The scoring information of the target projected image is determined 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.

5. The apparatus according to claim 4, characterized in that, The projection image determination module is specifically used to obtain target projection images of the point cloud to be evaluated at multiple preset angles based on a preset projection method.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, it implements the point cloud quality assessment method according to any one of claims 1-3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the point cloud quality assessment method according to any one of claims 1-3.

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