Abnormality detection method and device, electronic equipment and storage medium

Abstract

The invention relates to an anomaly detection method and device, electronic equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a to-be-detected three-dimensional model; carrying out model image acquisition on the to-be-detected three-dimensional model to obtain to-be-compared image sets corresponding to a plurality of acquisition view angles; based on the original position model image corresponding to each acquisition view angle and the respective offset position model image of the at least one offset acquisition position corresponding to each acquisition view angle, performing color difference analysis on each acquisition view angle to obtain a color difference analysis result corresponding to each acquisition view angle; and generating a rendering detection result based on the color difference analysis result corresponding to each collection view angle, the rendering detection result being used for indicating whether a depth conflict condition exists in the rendering process of the to-be-detected three-dimensional model. According to the embodiment of the invention, whether a depth conflict condition exists in the to-be-detected three-dimensional model or not can be detected, and the depth conflict detection efficiency and detection accuracy can be improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to an anomaly detection method and device, electronic equipment and storage medium. BACKGROUND

[0002] Depth conflict phenomenon often occurs in the display process of a three-dimensional model. The depth conflict (z-fighting) phenomenon refers to the phenomenon that the surfaces of two or more objects compete to be displayed at the same position in 3D rendering, resulting in flickering or jittering effect in vision. This is usually caused by insufficient precision of the depth buffer (z-buffer) or rendering order problem. The depth buffer is a buffer used to store the depth information of each pixel, which is used to determine which objects are displayed in the foreground or background. When the depth values of two objects are very close, the depth buffer may not accurately distinguish them, resulting in the occurrence of the depth conflict phenomenon. For example, in the field of game making, three-dimensional models play a very important role. They are the basis for constructing virtual worlds in games, and are used to present elements such as characters, scenes, props and special effects in games. Large game companies often have a large number of 3D models for game making projects. At the same time, in the 3D models in this field, depth conflict is a common problem, mainly occurring between two graphics that almost overlap on the same plane, resulting in flickering or zebra-like visual effects during rendering.

[0003] At present, there are many technologies about how to repair or avoid the depth conflict phenomenon, but these technologies are often subsequent work after detection. At present, the detection of the depth conflict phenomenon still depends on manual observation. However, it is time-consuming, labor-intensive and costly to find out whether a 3D model has the depth conflict phenomenon through manual observation. Therefore, the above depth conflict detection method for three-dimensional models has the problem of low detection efficiency. SUMMARY

[0004] In view of the above existing technical problems, the present disclosure provides an anomaly detection method, device, electronic equipment and storage medium.

[0005] According to an aspect of an embodiment of the present disclosure, an anomaly detection method is provided, and the method comprises:

[0006] obtaining a three-dimensional model to be detected;

[0007] The model image acquisition module is configured to acquire model images of the to-be-detected three-dimensional model, to obtain a plurality of to-be-compared image sets corresponding to respective acquisition angles; each to-be-compared image set corresponding to each acquisition angle includes an original position model image acquired at an original acquisition position corresponding to each acquisition angle and at least one offset position model image acquired at a respective offset acquisition position corresponding to each acquisition angle; the distance between the at least one offset acquisition position corresponding to each acquisition angle and the original acquisition position corresponding to each acquisition angle is less than a preset distance;

[0008] The color difference analysis module is configured to perform color difference analysis on each acquisition angle based on the original position model image corresponding to each acquisition angle and the respective offset position model image of the at least one offset acquisition position corresponding to each acquisition angle, to obtain a color difference analysis result corresponding to each acquisition angle.

[0009] The detection result generation module is configured to generate a rendering detection result corresponding to the to-be-detected three-dimensional model based on the color difference analysis result corresponding to each acquisition angle, where the rendering detection result is used to indicate whether a depth conflict exists in a rendering process of the to-be-detected three-dimensional model.

[0010] According to another aspect of the embodiments of the present disclosure, an abnormality detection device is provided, and the device includes:

[0011] The model acquisition module is configured to acquire a to-be-detected three-dimensional model.

[0012] The model image acquisition module is configured to acquire model images of the to-be-detected three-dimensional model, to obtain a plurality of to-be-compared image sets corresponding to respective acquisition angles; each to-be-compared image set corresponding to each acquisition angle includes an original position model image acquired at an original acquisition position corresponding to each acquisition angle and at least one offset position model image acquired at a respective offset acquisition position corresponding to each acquisition angle; the distance between the at least one offset acquisition position corresponding to each acquisition angle and the original acquisition position corresponding to each acquisition angle is less than a preset distance.

[0013] The color difference analysis module is configured to perform color difference analysis on each acquisition angle based on the original position model image corresponding to each acquisition angle and the respective offset position model image of the at least one offset acquisition position corresponding to each acquisition angle, to obtain a color difference analysis result corresponding to each acquisition angle.

[0014] The detection result generation module is configured to generate a rendering detection result corresponding to the to-be-detected three-dimensional model based on the color difference analysis result corresponding to each acquisition angle, where the rendering detection result is used to indicate whether a depth conflict exists in a rendering process of the to-be-detected three-dimensional model.

[0015] Optionally, the device further comprises:

[0016] a mesh color updating module, configured to perform mesh color updating processing on a plurality of meshes in the to-be-detected three-dimensional model based on a plurality of preset color information, to obtain an updated to-be-detected three-dimensional model; a color difference degree between each two pieces of preset color information in the plurality of preset color information is greater than a preset color difference degree;

[0017] Correspondingly, the model image acquisition module comprises:

[0018] a first acquisition sub-module, configured to perform model image acquisition on the updated to-be-detected three-dimensional model, to obtain the to-be-compared image set.

[0019] Optionally, the mesh color updating module comprises:

[0020] a current mesh determination sub-module, configured to determine a current mesh from the plurality of meshes; the current mesh is any one of the plurality of meshes whose color configuration information is not updated;

[0021] a color determination sub-module, configured to determine target color information from the plurality of preset color information; the target color information is any one of the plurality of preset color information;

[0022] a color configuration updating sub-module, configured to update color configuration information corresponding to the current mesh in the to-be-detected three-dimensional model based on the target color information, and repeat the steps of determining a current mesh from the plurality of meshes, to the step of updating color configuration information corresponding to the current mesh in the to-be-detected three-dimensional model based on the target color information, until the current updated mesh in the plurality of meshes meets a preset convergence condition;

[0023] a model determination sub-module, configured to take the to-be-detected three-dimensional model when the preset convergence condition is met as the updated to-be-detected three-dimensional model.

[0024] Optionally, the color difference analysis module comprises:

[0025] a first analysis sub-module, configured to perform color comparison analysis on the original position model image corresponding to each acquisition view angle and the target comparison image corresponding to each acquisition view angle, to obtain at least one first color comparison result corresponding to each acquisition view angle; the target comparison image corresponding to each acquisition view angle is any one of the offset position model images corresponding to each acquisition view angle;

[0026] The first result generation submodule is configured to generate a color difference analysis result corresponding to each of the collection perspectives based on the at least one first color comparison result corresponding to each of the collection perspectives.

[0027] Optionally, the first result generation submodule includes:

[0028] The first result determination unit is configured to determine a second color comparison result corresponding to each of the collection perspectives from the at least one first color comparison result corresponding to each of the collection perspectives. The second color comparison result corresponding to each of the collection perspectives is a color comparison result indicating the greatest color difference in the at least one first color comparison result corresponding to each of the collection perspectives.

[0029] The second result determination unit is configured to take the second color comparison result corresponding to each of the collection perspectives as the color difference analysis result corresponding to each of the collection perspectives.

[0030] Optionally, the second color comparison result corresponding to each of the collection perspectives includes first difference indicator data, which is used to indicate the degree of color difference between the corresponding two model images. The second result determination unit includes:

[0031] The difference indicator determination subunit is configured to determine second difference indicator data from the plurality of first difference indicator data corresponding to the plurality of collection perspectives. The second difference indicator data is the greatest first difference indicator data in the plurality of first difference indicator data corresponding to the plurality of collection perspectives.

[0032] The comparison processing subunit is configured to perform comparison processing on the second difference indicator data and preset difference indicator data to obtain a first indicator comparison result.

[0033] The result generation subunit is configured to generate the rendering detection result based on the first indicator comparison result.

[0034] Optionally, the result generation subunit includes:

[0035] The result determination subunit is configured to take a first detection result as the rendering detection result in a case where the first indicator comparison result indicates that the second difference indicator data is greater than the preset difference indicator data. The first detection result is used to indicate that there is a depth conflict in the rendering process of the to-be-detected three-dimensional model.

[0036] Optionally, the color difference analysis module includes:

[0037] The image pair generation submodule is configured to generate at least one to-be-compared image pair corresponding to each of the collection perspectives based on each two model images in the to-be-compared image set corresponding to each of the collection perspectives.

[0038] The second analysis submodule is used to perform color comparison analysis on the two model images in each image pair to be compared, and to obtain the third color comparison result corresponding to each image pair to be compared.

[0039] The second result generation submodule is used to generate color difference analysis results for each acquisition viewpoint based on at least one third color comparison result corresponding to each acquisition viewpoint.

[0040] Optionally, the color difference analysis result corresponding to each acquisition viewpoint includes at least one fourth color comparison result, each fourth color comparison result includes color difference index data, and the color difference index data in each fourth color comparison result is used to indicate the degree of color difference between the corresponding two model images; the detection result generation module includes:

[0041] The result determination submodule is used to determine the fifth color comparison result based on the color difference index data in the multiple fourth color comparison results corresponding to the multiple acquisition perspectives; the fifth color comparison result is the comparison result with the largest corresponding color difference index data in the multiple fourth color comparison results corresponding to the multiple acquisition perspectives.

[0042] The comparison processing submodule is used to compare the color difference index data in the fifth color comparison result with the preset difference index data to obtain the second index comparison result.

[0043] The third result generation submodule is used to generate the rendering detection result based on the comparison result of the second indicator.

[0044] Optionally, the device further includes:

[0045] The location information acquisition module is used to acquire the original acquisition location information of the original acquisition location corresponding to each of the multiple acquisition viewpoints; the original acquisition location information corresponding to each acquisition viewpoint is used to characterize the relative position of the original acquisition location corresponding to each acquisition viewpoint with respect to the origin position of the three-dimensional model to be detected.

[0046] The location information determination module is used to determine at least one offset acquisition location information corresponding to each acquisition viewpoint based on the original acquisition location information corresponding to the original acquisition location of each of the multiple acquisition viewpoints; the at least one offset acquisition location information is the location information of at least one offset acquisition location corresponding to each acquisition viewpoint;

[0047] Accordingly, the model image acquisition module includes:

[0048] The second acquisition submodule is used to acquire model images of the three-dimensional model to be detected based on at least one offset acquisition position information corresponding to each acquisition viewpoint and the original acquisition position information corresponding to each acquisition viewpoint, so as to obtain the set of images to be compared.

[0049] Optionally, the location information determination module includes:

[0050] The acquisition distance determination submodule is used to determine the target acquisition distance for each acquisition viewpoint based on the original acquisition position information corresponding to each acquisition viewpoint; the target acquisition distance for each acquisition viewpoint is the distance between the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected;

[0051] The acquisition plane determination submodule is used to determine the target acquisition plane corresponding to each acquisition viewpoint based on the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected; the target acquisition plane is a plane perpendicular to the target acquisition line corresponding to each acquisition viewpoint, and the original acquisition position corresponding to each acquisition viewpoint belongs to the target acquisition plane, and the target acquisition line corresponding to each acquisition viewpoint is the line connecting the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected;

[0052] The region radius determination submodule is used to determine the offset region radius corresponding to each acquisition viewpoint based on the target acquisition distance corresponding to each acquisition viewpoint; the offset region radius corresponding to each acquisition viewpoint is smaller than the target acquisition distance corresponding to each acquisition viewpoint.

[0053] The planar region determination submodule is used to determine the target acquisition plane region corresponding to each acquisition viewpoint based on the offset region radius corresponding to each acquisition viewpoint and the target acquisition plane corresponding to each acquisition viewpoint; the target acquisition plane region is a circular region in the target acquisition plane corresponding to each acquisition viewpoint with the original acquisition position corresponding to each acquisition viewpoint as the origin and the offset region radius corresponding to each acquisition viewpoint as the radius;

[0054] The location information determination submodule is used to determine the offset acquisition position information of at least one offset acquisition position corresponding to each acquisition viewpoint based on the target acquisition plane area corresponding to each acquisition viewpoint; the at least one offset acquisition position corresponding to each acquisition viewpoint is located within the target acquisition plane area corresponding to each acquisition viewpoint.

[0055] Optionally, the device further includes:

[0056] The detection result acquisition module is used to respond to the model search request and acquire multiple candidate 3D models and the rendering detection results corresponding to each of the multiple candidate 3D models;

[0057] The model filtering module is used to determine at least one 3D model to be displayed corresponding to the model search request from the plurality of candidate 3D models based on the rendering detection results; the rendering detection result corresponding to the at least one 3D model to be displayed is a second detection result, which is used to indicate that there is no depth conflict during the rendering process of the 3D model to be detected.

[0058] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the above-described anomaly detection method.

[0059] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the above-described anomaly detection method.

[0060] According to another aspect of the present disclosure, a computer program product containing instructions is provided that, when run on a computer, causes the computer to perform the above-described anomaly detection method.

[0061] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects:

[0062] By acquiring the 3D model to be detected, image acquisition is performed on the model to be detected, resulting in a set of comparison images corresponding to multiple acquisition viewpoints. Each set of comparison images for each acquisition viewpoint includes the original position model image acquired at the original acquisition position for each acquisition viewpoint and the offset position model image acquired at at least one offset acquisition position for each acquisition viewpoint. The distance between the at least one offset acquisition position and the original acquisition position for each acquisition viewpoint is less than a preset distance. This allows for the acquisition of model images of the 3D model to be detected at multiple acquisition positions under multiple acquisition viewpoints. Then, by combining the original position model image and the offset position model image at at least one offset acquisition position for each acquisition viewpoint, color difference analysis is performed on each acquisition viewpoint to obtain the color difference analysis results for each acquisition viewpoint. This allows for color difference analysis between model images acquired at multiple acquisition positions under each acquisition viewpoint. Finally, by combining the color difference analysis results for each acquisition viewpoint, a rendering detection result for the 3D model to be detected is generated. This enables the detection of depth conflicts in the 3D model to be detected, improving the efficiency and accuracy of depth conflict detection.

[0063] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0065] Figure 1 This is a schematic diagram illustrating an application system according to an exemplary embodiment;

[0066] Figure 2 This is a flowchart illustrating an anomaly detection method according to an exemplary embodiment;

[0067] Figure 3 This is a schematic diagram illustrating the positional relationship between an image acquisition device and a three-dimensional model to be detected during an image acquisition process, according to an exemplary embodiment.

[0068] Figures 4(a)(b)(c)(d) are schematic diagrams comparing the effects of a 3D model before and after mesh color update processing in an anomaly detection method according to an exemplary embodiment.

[0069] Figures 5(a)(b)(c)(d)(e) are schematic diagrams illustrating a set of images to be compared acquired from a 3D model with depth conflicts, according to an exemplary embodiment.

[0070] Figures 6(a)(b)(c)(d)(e) are schematic diagrams illustrating a set of images to be compared acquired from a 3D model without depth conflict, according to an exemplary embodiment.

[0071] Figure 7 This is a block diagram illustrating an anomaly detection device according to an exemplary embodiment;

[0072] Figure 8 This is a block diagram illustrating an electronic device for implementing anomaly detection of a 3D model to be inspected, according to an exemplary embodiment.

[0073] Figure 9 This is a block diagram illustrating another electronic device for implementing anomaly detection for a 3D model to be detected, according to an exemplary embodiment. Detailed Implementation

[0074] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0075] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0076] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0077] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application system according to an exemplary embodiment. The application system can be used in the anomaly detection method of this application. The application system may include at least a server 01 and a terminal 02.

[0078] In this embodiment, server 01 can be used to detect the 3D model to be detected. Specifically, server 01 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0079] In this embodiment, terminal 02 can be used to generate a 3D model to be detected. Terminal 02 may include physical devices such as smartphones, desktop computers, tablets, laptops, smart speakers, in-vehicle terminals, digital assistants, augmented reality (AR) / virtual reality (VR) devices, and smart wearable devices, and may also include software running on the physical device, such as applications. The operating system running on terminal 02 in this embodiment may include, but is not limited to, Android, GNU / Linux, and Windows systems.

[0080] In addition, it should be noted that, Figure 1 The example shown is merely one application environment provided by this disclosure. In practical applications, other application environments may also be included. For example, the detection process of the three-dimensional model to be detected can also be implemented on terminal 02.

[0081] In the embodiments described in this specification, the terminal 02 and the server 01 can be directly or indirectly connected through wired or wireless communication, and this application does not limit this connection.

[0082] It should be noted that the following diagram shows one possible sequence of steps, and it is not strictly necessary to follow this order. Some steps can be executed in parallel without interdependence.

[0083] Specifically, Figure 2 This is a flowchart illustrating an anomaly detection method according to an exemplary embodiment. For example... Figure 2 As shown, this anomaly detection method can be executed by electronic devices such as terminals or servers, and specifically may include the following steps:

[0084] S201: Obtain the 3D model to be detected.

[0085] In one specific embodiment, the 3D model to be detected can be a 3D model for which depth conflicts need to be detected. The 3D model to be detected can be rendered based on the model information corresponding to the aforementioned 3D model and a preset model rendering tool. The model information corresponding to the aforementioned 3D model to be detected can be used to implement the rendering of the 3D model to be detected. The model information corresponding to the 3D model to be detected may include the geometric shape data and material attribute data of the 3D model to be detected.

[0086] In one specific embodiment, a preset model file of the 3D model to be detected can be obtained; the preset model file can be loaded to add the 3D model to be detected to a 3D scene; an image acquisition device can be added to the 3D scene; and the 3D model to be detected can be scaled so that its entire content exists within the field of view of the image acquisition device. The 3D scene can provide the background and environment for the existence and interaction of the 3D model. The 3D scene may include elements such as ground, sky, buildings, or props. The image acquisition device can be a virtual device used to simulate the human perspective and image capture process in the real world.

[0087] In one specific embodiment, the center point of the 3D model to be detected can be positioned at the origin of the 3D scene by setting its position. Specifically, this can be done before loading by setting the initial position information of the model to be detected at the origin of the 3D scene. Alternatively, after loading the 3D model to be detected, a position movement operation can be performed on the model to move it, so that the center point of the model is moved to the origin of the 3D scene.

[0088] In one specific embodiment, when the center point of the 3D model to be detected is located at the origin of the 3D scene, the bounding box of the 3D model to be detected can be scaled to between -1 and 1 of the 3D coordinate axes in the 3D scene, so that the entire contents of the 3D model to be detected exist in the field of view of the image acquisition device.

[0089] S203: Acquire model images of the 3D model to be detected, and obtain a set of images to be compared corresponding to multiple acquisition perspectives.

[0090] In one specific embodiment, the acquisition viewpoint can be used to indicate the orientation and position of the image acquisition device relative to the 3D model to be detected. The acquisition viewpoint can include a bird's-eye view, a top-down view, or a level-on view, etc.; further, the level-on viewpoint can include a first level-on viewpoint, a second level-on viewpoint, a third level-on viewpoint, or a fourth level-on viewpoint; wherein, a top-down viewpoint can refer to a viewpoint where the image acquisition device is located diagonally above and downwards from the 3D model; a bird's-eye view can refer to a viewpoint where the image acquisition device is located above the 3D model and downwards from the model; the first and second level-on viewpoints can be used to acquire rendered images of the front and rear areas of the 3D model to be detected, respectively; the third and fourth level-on viewpoints can be used to acquire rendered images of the left and right areas of the 3D model to be detected, respectively. It is understood that images of different areas of the 3D model to be detected can be acquired based on different acquisition viewpoints. Specifically, the acquisition viewpoint can be set according to actual needs, and this disclosure does not limit it. For example, in this embodiment, multiple acquisition viewpoints can include the aforementioned top-down viewpoint and level-on viewpoint, and the level-on viewpoint can include a first level-on viewpoint, a second level-on viewpoint, a third level-on viewpoint, and a fourth level-on viewpoint.

[0091] In a specific embodiment, the set of images to be compared for each acquisition viewpoint may include the original position model image acquired at the original acquisition position corresponding to each acquisition viewpoint and the offset position model images acquired at at least one offset acquisition position corresponding to each acquisition viewpoint. The original acquisition position corresponding to any acquisition viewpoint can refer to the position of the image acquisition device in the 3D scene when the image acquisition device is located at any of the aforementioned acquisition viewpoints. Any offset acquisition position corresponding to any acquisition viewpoint can be a position that deviates from the original acquisition position corresponding to any of the aforementioned acquisition viewpoints. Specifically, the deviation between any offset acquisition position corresponding to any acquisition viewpoint and the original acquisition position corresponding to any of the aforementioned acquisition viewpoints is less than a preset deviation. It is understood that for 3D models with depth conflicts, when the position of the image acquisition device changes, it will change the relative position of the relevant mesh patches in the field of view, thereby further affecting their accuracy and resolution in the depth buffer, ultimately leading to changes in the visual effect.

[0092] In a specific embodiment, the original position model image corresponding to any acquisition viewpoint can refer to the rendered image obtained by rendering the 3D model to be detected based on the image acquisition device being located at any of the acquisition viewpoints.

[0093] In a specific embodiment, the offset position model image corresponding to any offset acquisition position of any acquisition viewpoint can refer to the rendered image obtained by rendering the three-dimensional model to be detected based on the image acquisition device being located at any offset acquisition position corresponding to any acquisition viewpoint and the acquisition orientation being the orientation corresponding to any acquisition viewpoint.

[0094] In one specific embodiment, the distance between at least one offset acquisition position corresponding to each acquisition viewpoint and the original acquisition position corresponding to each acquisition viewpoint can both be less than a preset distance. Specifically, the preset distance can be set according to actual application needs, and this disclosure does not limit it. Optionally, the preset distance can be obtained by multiplying the distance between the image acquisition device and the origin position of the 3D model to be detected by a preset percentage. The preset percentage can be set according to actual needs. For example, the preset percentage can be 5%.

[0095] In a specific embodiment, the line connecting any offset acquisition position corresponding to any acquisition viewpoint and the original acquisition position corresponding to any acquisition viewpoint can be perpendicular to the line connecting the original acquisition position corresponding to any acquisition viewpoint and the origin position of the 3D model to be detected.

[0096] In one specific embodiment, the above method may further include:

[0097] Obtain the original acquisition location information corresponding to the original acquisition location of multiple acquisition perspectives;

[0098] Based on the original acquisition position information corresponding to the original acquisition positions of multiple acquisition viewpoints, at least one offset acquisition position information corresponding to each acquisition viewpoint is determined.

[0099] Accordingly, the above-mentioned model image acquisition of the 3D model to be detected, resulting in multiple sets of comparison images corresponding to different acquisition perspectives, may include:

[0100] Based on at least one offset acquisition position information corresponding to each acquisition viewpoint and the original acquisition position information corresponding to each acquisition viewpoint, model image acquisition is performed on the 3D model to be detected to obtain a set of images to be compared.

[0101] In one specific embodiment, the original acquisition position information corresponding to each acquisition viewpoint can be used to characterize the relative position of the original acquisition position of each acquisition viewpoint with respect to the origin position of the 3D model to be detected. The original acquisition position information corresponding to each acquisition viewpoint can be represented in various forms, such as coordinates or descriptive information describing the position in natural language. It is understood that when the origin position of the 3D model to be detected coincides with the origin position in the 3D scene, the original acquisition position information corresponding to any of the above acquisition viewpoints can be the coordinates of the 3D scene in which the image acquisition device is located when it is at any of the above acquisition viewpoints.

[0102] In a specific embodiment, any offset acquisition position information corresponding to any acquisition viewpoint can refer to the position information of any offset acquisition position corresponding to any acquisition viewpoint. The original acquisition position information corresponding to any acquisition viewpoint can be represented in various forms, such as coordinates or descriptive information describing the position in natural language.

[0103] In one specific embodiment, determining at least one offset acquisition position information corresponding to each acquisition viewpoint based on the original acquisition position information corresponding to the original acquisition positions of multiple acquisition viewpoints may include:

[0104] Based on the original acquisition location information corresponding to each acquisition viewpoint, the target acquisition distance corresponding to each acquisition viewpoint is determined;

[0105] Based on the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected, the target acquisition plane corresponding to each acquisition viewpoint is determined.

[0106] Based on the target acquisition distance corresponding to each acquisition viewpoint, determine the radius of the offset region corresponding to each acquisition viewpoint;

[0107] Based on the offset region radius corresponding to each acquisition viewpoint and the target acquisition plane corresponding to each acquisition viewpoint, the target acquisition plane region corresponding to each acquisition viewpoint is determined.

[0108] Based on the target acquisition plane area corresponding to each acquisition viewpoint, determine the offset acquisition position information of at least one offset acquisition position corresponding to each acquisition viewpoint.

[0109] In one specific embodiment, the target acquisition distance corresponding to each acquisition viewpoint can be the distance between the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected. The origin position of the 3D model to be detected can be the geometric center point of the 3D model to be detected or the bounding box center point of the 3D model to be detected, etc.

[0110] In a specific embodiment, the distance between the original acquisition position corresponding to any acquisition viewpoint and the origin position of the 3D model to be detected can be determined based on the original acquisition position information corresponding to any acquisition viewpoint, thereby obtaining the target acquisition distance corresponding to any acquisition viewpoint.

[0111] In one specific embodiment, the target acquisition plane corresponding to each acquisition viewpoint can be a plane perpendicular to the target acquisition line corresponding to each acquisition viewpoint. The original acquisition position corresponding to each acquisition viewpoint can belong to the target acquisition plane. The target acquisition line corresponding to each acquisition viewpoint can be a line connecting the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected.

[0112] In a specific embodiment, the position of the target acquisition line corresponding to any acquisition viewpoint in the 3D scene can be determined based on the original acquisition position corresponding to any acquisition viewpoint and the origin position of the 3D model to be detected. Correspondingly, the target acquisition plane corresponding to any acquisition viewpoint can be determined based on the target acquisition line corresponding to any acquisition viewpoint and the original acquisition position corresponding to any acquisition viewpoint. Specifically, a plane perpendicular to the target acquisition line corresponding to any acquisition viewpoint and passing through the position point of the original acquisition position corresponding to any acquisition viewpoint can be used as the target acquisition plane corresponding to any acquisition viewpoint.

[0113] In a specific embodiment, the radius of the offset region corresponding to any acquisition viewpoint can refer to the radius of the offset region corresponding to any acquisition viewpoint. The offset region of any acquisition viewpoint can be used to indicate the offset range of at least one offset acquisition position relative to the original acquisition position corresponding to any acquisition viewpoint. It is understood that the distance between the offset acquisition position of any acquisition viewpoint and the offset acquisition position corresponding to any acquisition viewpoint cannot exceed the radius of the offset region corresponding to any acquisition viewpoint. This avoids significant changes in the two-dimensional contour of the projected image acquisition device's field of view, which could affect the accuracy of subsequent color difference analysis. It also prevents the model from jumping out of the image acquisition device's field of view after significant movement.

[0114] In one specific embodiment, the radius of the offset region corresponding to each acquisition viewpoint can be smaller than the target acquisition distance corresponding to each acquisition viewpoint.

[0115] In one specific embodiment, the target acquisition distance corresponding to any acquisition viewpoint can be multiplied by a preset percentage to obtain the radius of the offset region corresponding to any acquisition viewpoint. Specifically, the preset percentage can be set according to actual application needs. Optionally, the value range of the preset percentage can be (0, 10%). For example, the preset percentage can be 5%.

[0116] In one specific embodiment, the target acquisition plane region can be used to indicate the offset range of the offset acquisition position corresponding to any original acquisition position relative to any of the original acquisition positions. The target acquisition plane region can be a circular area in the target acquisition plane corresponding to each acquisition viewpoint, with the original acquisition position corresponding to each acquisition viewpoint as the origin and the radius of the offset area corresponding to each acquisition viewpoint as the radius.

[0117] In a specific embodiment, a circular region in the target acquisition plane corresponding to any acquisition viewpoint, with the original acquisition position corresponding to the above-mentioned acquisition viewpoint as the origin and the radius of the offset region corresponding to the above-mentioned acquisition viewpoint as the radius, can be used as the target acquisition plane region corresponding to any acquisition viewpoint.

[0118] In one specific embodiment, the location point corresponding to any offset acquisition position can be located within the target acquisition plane area corresponding to any of the offset acquisition positions.

[0119] In one specific embodiment Figure 3 This is a schematic diagram illustrating the positional relationship between an image acquisition device and a 3D model to be detected during an image acquisition process, according to an exemplary embodiment. Specifically, as shown... Figure 3 As shown, the target acquisition distance for each acquisition viewpoint can be determined based on the original acquisition position information corresponding to each acquisition viewpoint; the target acquisition plane for each acquisition viewpoint can be determined based on the original acquisition position and the origin position of the 3D model to be detected; the offset region radius for each acquisition viewpoint can be determined based on the target acquisition distance for each acquisition viewpoint; and the target acquisition plane region for each acquisition viewpoint can be determined based on the offset region radius and the target acquisition plane for each acquisition viewpoint. Accordingly, an offset acquisition position can be selected within the target acquisition plane region to acquire model images of the 3D model to be detected, thereby obtaining a set of images to be compared.

[0120] In one specific embodiment, any location within the target acquisition plane area corresponding to any acquisition viewpoint can be used as the target offset acquisition location corresponding to that acquisition viewpoint; the location information of any location within the target acquisition plane area corresponding to that acquisition viewpoint can be used as the location information of the target offset acquisition location corresponding to that acquisition viewpoint. Accordingly, offset acquisition location information for at least one offset acquisition location corresponding to each acquisition viewpoint can be obtained. Here, the target offset acquisition location corresponding to any acquisition viewpoint can refer to any one of the at least one offset acquisition location corresponding to that acquisition viewpoint.

[0121] In the above embodiments, by determining the target acquisition distance for each acquisition viewpoint based on the original acquisition position information corresponding to each acquisition viewpoint, determining the target acquisition plane for each acquisition viewpoint based on the original acquisition position and the origin position of the 3D model to be detected based on the original acquisition position for each acquisition viewpoint, determining the offset region radius for each acquisition viewpoint based on the target acquisition distance for each acquisition viewpoint, determining the target acquisition plane region for each acquisition viewpoint based on the offset region radius and the target acquisition plane for each acquisition viewpoint, and determining the offset acquisition position information for at least one offset acquisition position for each acquisition viewpoint based on the target acquisition plane region for each acquisition viewpoint, it is possible to ensure that the distance of each offset acquisition position relative to the corresponding original acquisition position is less than a preset distance. This facilitates the realization of small offsets of the image acquisition device, thereby ensuring that the contours in the model images acquired at different positions under any acquisition viewpoint remain unchanged, while also reflecting the color differences between the model images at different positions. In addition, it can also avoid large movements that cause parts of the model to jump out of the acquisition field of view of the image acquisition device, thereby further improving the accuracy of depth conflict detection for 3D models.

[0122] In a specific embodiment, the image acquisition device can be set at the original acquisition position corresponding to any of the acquisition viewpoints based on the original acquisition position information. When the image acquisition device is located at the original acquisition position corresponding to any of the acquisition viewpoints, the three-dimensional model to be detected can be rendered based on any of the acquisition viewpoints to obtain the original position model image acquired at the original acquisition position corresponding to any of the acquisition viewpoints.

[0123] In a specific embodiment, the image acquisition device can be set at any of the offset acquisition positions based on the offset acquisition position information corresponding to any of the acquisition viewpoints. When the image acquisition device is located at any of the offset acquisition positions, the three-dimensional model to be detected can be rendered based on any of the acquisition viewpoints to obtain the offset position model image acquired at any of the offset acquisition positions corresponding to any of the acquisition viewpoints.

[0124] In the above embodiments, the offset acquisition position information of any offset acquisition position corresponding to each acquisition view is determined by the target acquisition plane area corresponding to each acquisition view. Based on at least one offset acquisition position information corresponding to each acquisition view and the original acquisition position information corresponding to each acquisition view, the model image of the 3D model to be detected is acquired to obtain a set of images to be compared. This can realize the acquisition of model images at different positions under each acquisition view, and can keep the contours in the model images at different positions acquired under any acquisition view unchanged, avoiding the impact of image contour changes on the accuracy of subsequent color difference analysis, that is, it can improve the accuracy of color difference analysis, and further improve the accuracy of depth conflict detection of 3D models.

[0125] In one specific embodiment, the above method may further include:

[0126] Based on multiple preset color information, the mesh color of multiple meshes in the 3D model to be detected is updated to obtain the updated 3D model to be detected.

[0127] Accordingly, the above-mentioned model image acquisition of the 3D model to be detected, resulting in multiple sets of comparison images corresponding to different acquisition perspectives, may include:

[0128] The updated 3D model to be detected is image acquired to obtain a set of images to be compared.

[0129] In one specific embodiment, any preset color information can be used to indicate any one of a plurality of different preset colors. Any preset color information may include color indication information or color description information, etc. Specifically, the preset color information may be set according to actual application needs, and this disclosure does not limit it. For example, the plurality of preset colors corresponding to multiple preset color information may include bright yellow, bright green, or bright red, etc.

[0130] In one specific embodiment, the brightness corresponding to each preset color information in the plurality of preset color information can be greater than a preset brightness. Specifically, the preset brightness can be set according to the actual application needs, and this disclosure does not limit it.

[0131] In one specific embodiment, the saturation corresponding to each preset color information in the plurality of preset color information can be greater than a preset saturation. Specifically, the preset saturation can be set according to the actual application needs, and this disclosure does not limit it.

[0132] In one specific embodiment, the color difference between any two preset color information items can be greater than a preset color difference level. Specifically, the contrast between any two preset color information items is greater than a preset contrast ratio. Alternatively, the distance between the corresponding positions of any two preset color information items on the color wheel can be greater than a preset hue interval.

[0133] In one specific embodiment, the number of colors in the multiple preset color information is greater than a preset number. It is understood that, given that the degree of difference between the preset color information is greater than a preset degree of difference, the more preset color information there are, the richer the colors in the rendered image after updating the mesh colors, and the more pronounced the visual effect will be when depth conflicts exist.

[0134] In one specific embodiment, the colors corresponding to multiple preset color information can be evenly distributed in the color space.

[0135] In one specific embodiment, the updated 3D model to be detected can refer to the 3D model obtained by updating the mesh colors of all or part of the aforementioned multiple meshes. It is understood that the mesh color update processing can be performed on all meshes in the 3D model to be detected, or it can be performed on a portion of the multiple meshes. For example, in this embodiment, the mesh color update processing is performed on all meshes in the 3D model to be detected, resulting in the updated 3D model to be detected.

[0136] In a specific embodiment, during the mesh color update process for the target mesh in the 3D model to be detected, a preset color information can be randomly selected from the multiple preset color information, and the target mesh can be updated based on the selected preset color information. The target mesh can be any one of the multiple meshes. Accordingly, the updated 3D model to be detected can be obtained. It is understood that for each mesh in the multiple meshes of the 3D model to be detected, the mesh color can be updated using any randomly selected preset color information, resulting in a large color difference between different meshes in the updated 3D model to be detected, which will make the visual effect more obvious when there is depth conflict.

[0137] In one specific embodiment, Figures 4(a), (b), (c), and (d) are schematic diagrams comparing the effects of a 3D model before and after mesh color update processing in an anomaly detection method according to an exemplary embodiment. Specifically, as an example, Figure 4(a) shows the effect of a 3D model to be detected before mesh color update processing, and Figure 4(b) shows the effect of the model corresponding to Figure 4(a) after mesh color update processing. As another example, Figure 4(c) shows another 3D model to be detected before mesh color update processing, and Figure 4(d) shows the effect of the model corresponding to Figure 4(c) after mesh color update processing.

[0138] In a specific embodiment, the above-mentioned mesh color update processing of multiple meshes in the 3D model to be detected based on multiple preset color information to obtain the updated 3D model to be detected may include:

[0139] Determine the current grid from multiple grids;

[0140] Determine the target color information from multiple preset color information;

[0141] Based on the target color information, update the color configuration information corresponding to the current mesh in the 3D model to be detected, and repeat the mesh configuration update steps from multiple meshes to determine the current mesh, and update the color configuration information corresponding to the current mesh in the 3D model to be detected based on the target color information, until the currently updated mesh in multiple meshes meets the preset convergence condition.

[0142] The 3D model to be detected that meets the preset convergence conditions is used as the updated 3D model to be detected.

[0143] In one specific embodiment, the current grid can refer to the grid whose color configuration information is currently to be updated. The current grid can be any one of multiple grids whose color configuration information has not been updated.

[0144] In one specific embodiment, the grid whose color configuration information has not been updated can be determined from multiple grids; correspondingly, any one of the grids whose color configuration information has not been updated can be taken as the current grid.

[0145] In one specific embodiment, the target color information can be used to indicate the color of the current grid after updating. The target color information can be any one of multiple preset color information. Specifically, one preset color information can be randomly selected from multiple preset color information as the target color information.

[0146] In one specific embodiment, the 3D model to be detected may include appearance configuration data. The appearance configuration data can be used to describe the appearance and surface characteristics of the rendered 3D model. The appearance configuration data may include color configuration information corresponding to each mesh in multiple meshes. It is understood that the appearance and surface characteristics of the rendered object can be updated by updating the aforementioned appearance configuration data.

[0147] In one specific embodiment, the currently updated mesh can refer to a mesh whose color configuration information has been updated during the mesh color update process of the 3D model to be detected. Specifically, the currently updated mesh may include at least one of the above-mentioned meshes.

[0148] In one specific embodiment, the preset convergence condition may be that the number of currently updated grids is greater than or equal to a preset number of grids, or it may be that the currently updated grids are multiple grids as described above. Specifically, the preset number of grids can be set according to actual application needs, and this disclosure does not limit it. Optionally, the preset number of grids may be less than or equal to the number of the multiple grids mentioned above.

[0149] In one specific embodiment, when it is detected that the currently updated mesh in multiple meshes meets a preset convergence condition, the color configuration information of any two adjacent meshes in the current 3D model to be detected can be compared to obtain multiple color configuration comparison results. If any color configuration comparison result indicates that the color configuration information of the corresponding two adjacent meshes is the same, the mesh color of one of the two adjacent meshes is updated so that the color configuration information of the two adjacent meshes is different.

[0150] In the above embodiments, by determining the current grid from multiple grids, determining the target color information from multiple preset color information, updating the color configuration information corresponding to the current grid in the 3D model to be detected based on the target color information, and repeating the grid configuration update steps from determining the current grid from multiple grids to updating the color configuration information corresponding to the current grid in the 3D model to be detected based on the target color information, until the currently updated grid among multiple grids meets the preset convergence condition, the 3D model to be detected when the preset convergence condition is met is taken as the updated 3D model to be detected. This can realize the grid color update processing of multiple grids in the 3D model to be detected, and can avoid the situation where any grid is repeatedly updated multiple times, thus avoiding the waste of computing resources.

[0151] In one specific embodiment, after updating the mesh color of the 3D model to be detected, model images can be acquired based on at least one offset acquisition position information corresponding to each acquisition viewpoint and the original acquisition position information corresponding to each acquisition viewpoint, thereby obtaining a set of images to be compared. Specifically, after updating the mesh color of the 3D model to be detected, rendering processing can be performed on the 3D model to be detected at each acquisition position, thereby obtaining a set of images to be compared.

[0152] In a specific embodiment, Figures 5(a), (b), (c), (d), and (e) are schematic diagrams illustrating a set of comparison images acquired from a 3D model with depth conflicts, according to an exemplary embodiment. Specifically, Figure 5(a) can be obtained by acquiring the model image of the 3D model to be detected based on the original acquisition position when the acquisition viewpoint is at eye level; Figures 5(b), 5(c), 5(d), and 5(e) can be obtained by acquiring the model image of the 3D model to be detected based on multiple different offset acquisition positions corresponding to the original acquisition position when the acquisition viewpoint is at eye level. The 3D model to be detected is a 3D model with depth conflicts.

[0153] In a specific embodiment, Figures 6(a), 6(b), 6(c), 6(d), and 6(e) are schematic diagrams illustrating a set of comparison images acquired from a 3D model without depth conflict, according to an exemplary embodiment. Specifically, Figure 6(a) can be obtained by acquiring a model image of the 3D model to be detected based on the original acquisition position when the acquisition viewpoint is a level viewpoint; Figures 6(b), 6(c), 6(d), and 6(e) can be obtained by acquiring a model image of the 3D model to be detected based on each of multiple different offset acquisition positions corresponding to the original acquisition position when the acquisition viewpoint is a level viewpoint. The 3D model to be detected is a 3D model without depth conflict.

[0154] In the above embodiments, by combining multiple preset color information to update the mesh colors of multiple meshes in the 3D model to be detected, the visual representation of depth conflict can be enhanced for 3D models with depth conflict, thereby further improving the accuracy of subsequent depth conflict detection.

[0155] S205: Based on the original position model image corresponding to each acquisition viewpoint and the offset position model image of at least one offset acquisition position corresponding to each acquisition viewpoint, perform color difference analysis on each acquisition viewpoint to obtain the color difference analysis result corresponding to each acquisition viewpoint.

[0156] In one specific embodiment, the color difference analysis result corresponding to any acquisition viewpoint can be used to indicate the degree of color difference between multiple model images acquired under any acquisition viewpoint. The multiple model images acquired under any acquisition viewpoint can be the original position model image acquired at the original acquisition position corresponding to any acquisition viewpoint and the offset position model image acquired at at least one offset acquisition position corresponding to any acquisition viewpoint.

[0157] In one specific embodiment, step S205 above may include:

[0158] Color comparison analysis is performed on the original position model image and the target comparison image corresponding to each acquisition viewpoint to obtain at least one first color comparison result for each acquisition viewpoint.

[0159] Based on at least one first color comparison result corresponding to each acquisition viewpoint, generate color difference analysis results for each acquisition viewpoint.

[0160] In a specific embodiment, the target comparison image corresponding to any acquisition viewpoint can be any one of the offset position model images of at least one offset acquisition position corresponding to any of the above acquisition viewpoints.

[0161] In one specific embodiment, at least one first color comparison result corresponding to any acquisition viewpoint can refer to the color comparison result corresponding to each offset position model image in at least one offset position model image corresponding to the aforementioned acquisition viewpoint. Here, any color comparison result can be used to indicate the degree of color difference between corresponding two model images. Any color comparison result may include difference index data. The difference index data can be used to indicate the degree of color difference between corresponding two model images in data form.

[0162] In a specific embodiment, the first pixel color data corresponding to each of multiple first pixels in the original position model image corresponding to any acquisition viewpoint can be determined; based on the first pixel color data corresponding to each of the multiple first pixels, color analysis can be performed on the original position model image corresponding to any acquisition viewpoint to obtain the first image color data of the original position model image corresponding to any acquisition viewpoint; the second pixel color data corresponding to each of multiple second pixels in the target comparison image corresponding to any acquisition viewpoint can be determined; based on the second pixel color data corresponding to each of the multiple second pixels, color analysis can be performed on the target comparison image corresponding to any acquisition viewpoint to obtain the second image color data of the target comparison image corresponding to any acquisition viewpoint; data difference analysis can be performed on the first image color data and the second image color data to obtain the first color comparison result corresponding to the target comparison image under any acquisition viewpoint.

[0163] In one specific embodiment, the first pixel color data corresponding to any one of the plurality of first pixels can be used to indicate the color of any one of the first pixels. Specifically, the first pixel color data may include red channel data, blue channel data, and green channel data.

[0164] In one specific embodiment, the first image color data of the original position model image corresponding to any acquisition viewpoint can be used to describe the visual features of the original position model image corresponding to any acquisition viewpoint.

[0165] In one specific embodiment, the average value of the first pixel color data corresponding to each of the multiple first pixel points can be used as the first image color data.

[0166] In one specific embodiment, the second pixel color data corresponding to any one of the plurality of second pixels can be used to indicate the color of any one of the second pixels. Specifically, the second pixel color data may include red channel data, blue channel data, and green channel data.

[0167] In one specific embodiment, the second image color data of the target comparison image corresponding to any acquisition viewpoint can be used to describe the visual features of the target comparison image corresponding to any acquisition viewpoint.

[0168] In one specific embodiment, the average value of the second pixel color data corresponding to each of the multiple second pixel points can be used as the second image color data.

[0169] In a specific embodiment, the difference between the first image color data and the second image color data can be used as the first color comparison result corresponding to the target comparison image under any of the above acquisition perspectives.

[0170] In one specific embodiment, the ratio of the difference between the first image color data and the second image color data to the reference image color data can be used as the first color comparison result corresponding to the target comparison image under any of the above acquisition perspectives. The reference image color data can be determined based on the first image color data and the second image color data. Specifically, either the first image color data or the second image color data can be used as the reference image color data. Alternatively, the data with the larger absolute value among the first image color data and the second image color data can be used as the reference image color data. Alternatively, the average value of the first image color data and the second image color data can also be used as the reference image color data.

[0171] In a specific embodiment, at least one first color comparison result corresponding to any acquisition viewpoint can be used as the color difference analysis result corresponding to any acquisition viewpoint.

[0172] In a specific embodiment, the above-mentioned generation of color difference analysis results for each acquisition viewpoint based on at least one first color comparison result corresponding to each acquisition viewpoint may include:

[0173] From at least one first color comparison result corresponding to each acquisition viewpoint, determine the second color comparison result corresponding to each acquisition viewpoint;

[0174] The second color comparison result corresponding to each acquisition viewpoint is used as the color difference analysis result for each acquisition viewpoint.

[0175] In one specific embodiment, the second color comparison result corresponding to any acquisition viewpoint can be the color comparison result indicating the largest color difference among at least one first color comparison result corresponding to any acquisition viewpoint. Specifically, the second color comparison result corresponding to any acquisition viewpoint can be the first color comparison result with the largest corresponding difference index data among at least one first color comparison result corresponding to any acquisition viewpoint.

[0176] In the above embodiments, by determining the second color comparison result corresponding to each acquisition viewpoint from at least one first color comparison result corresponding to each acquisition viewpoint, and using the second color comparison result corresponding to each acquisition viewpoint as the color difference analysis result corresponding to each acquisition viewpoint, the accuracy of color difference analysis under each acquisition viewpoint can be improved.

[0177] In the above embodiments, by performing color comparison analysis on the original position model image and the target comparison image corresponding to each acquisition viewpoint, at least one first color comparison result corresponding to each acquisition viewpoint can be obtained. This enables color comparison analysis of the model images of the original acquisition position and the deviation acquisition position under each acquisition viewpoint. By combining the at least one first color comparison result corresponding to each acquisition viewpoint, color difference analysis results corresponding to each acquisition viewpoint can be generated. This enables efficient and accurate analysis of color differences of model images at different positions under each acquisition viewpoint.

[0178] In one specific embodiment, step S205 above may include:

[0179] Based on every two model images in the set of images to be compared corresponding to each acquisition viewpoint, at least one pair of images to be compared corresponding to each acquisition viewpoint is generated.

[0180] Color comparison analysis is performed on the two model images in each pair of images to be compared to obtain the third color comparison result corresponding to each pair of images to be compared.

[0181] Based on at least one third color comparison result corresponding to each acquisition viewpoint, generate color difference analysis results for each acquisition viewpoint.

[0182] In one specific embodiment, any pair of images to be compared corresponding to any acquisition viewpoint may include any two model images from the set of images to be compared corresponding to any acquisition viewpoint. It is understood that any pair of images to be compared may consist of offset position model images corresponding to two different offset acquisition positions under any acquisition viewpoint, or it may consist of the original position model image corresponding to the original acquisition position under any acquisition viewpoint and the offset position model image corresponding to any offset acquisition position under any acquisition viewpoint. At least one pair of images to be compared corresponding to any acquisition viewpoint may include all images from the set of images to be compared corresponding to any acquisition viewpoint.

[0183] In a specific embodiment, any two model images in the set of images to be compared corresponding to any acquisition viewpoint can be taken as a pair of images to be compared. Accordingly, at least one pair of images to be compared corresponding to any acquisition viewpoint can be obtained.

[0184] In one specific embodiment, the third color matching result corresponding to any pair of images to be compared can be used to indicate the degree of color difference between the two model images contained in any pair of images to be compared.

[0185] In a specific embodiment, the specific color comparison analysis process for the two model images in any pair of images to be compared can refer to the specific color comparison analysis process of the original position model image corresponding to any acquisition viewpoint and the target comparison image corresponding to any acquisition viewpoint, which will not be repeated here.

[0186] In a specific embodiment, at least one third color comparison result corresponding to any acquisition viewpoint can be used as the color difference analysis result corresponding to any acquisition viewpoint.

[0187] In a specific embodiment, the third color comparison result with the largest difference index data among at least one third color comparison result corresponding to any of the above acquisition perspectives can be taken as the color difference analysis result corresponding to any of the above acquisition perspectives.

[0188] In the above embodiments, based on every two model images in the image set to be compared corresponding to each acquisition viewpoint, at least one image pair to be compared corresponding to each acquisition viewpoint is generated. Color comparison analysis is performed on the two model images in each image pair to be compared to obtain the third color comparison result corresponding to each image pair to be compared. This can realize pairwise comparison of model images at multiple acquisition positions under each acquisition viewpoint. Then, combined with at least one third color comparison result corresponding to each acquisition viewpoint, the color difference analysis result corresponding to each acquisition viewpoint is generated, which can further improve the accuracy of the color difference analysis result.

[0189] S207: Based on the color difference analysis results corresponding to each acquisition viewpoint, generate the rendering detection results corresponding to the 3D model to be detected.

[0190] In one specific embodiment, the rendering detection results are used to indicate whether depth conflicts exist during the rendering process of the 3D model to be detected. The rendering detection results may include a first detection result or a second detection result.

[0191] In one specific embodiment, the first detection result can be used to indicate the presence of depth conflicts during the rendering process of the 3D model to be detected. The second detection result can be used to indicate that no depth conflicts exist during the rendering process of the 3D model to be detected.

[0192] In one specific embodiment, the second color comparison result corresponding to each acquisition viewpoint may include first difference index data. The first difference index data can be used to indicate the degree of color difference between the corresponding two model images.

[0193] In a specific embodiment, when the color difference analysis result corresponding to any of the above-mentioned acquisition viewpoints is the second color comparison result corresponding to any of the above-mentioned acquisition viewpoints, the above-mentioned generation of the rendering detection result corresponding to the 3D model to be detected based on the color difference analysis result corresponding to each acquisition viewpoint may include:

[0194] The second difference index data is determined from multiple first difference index data corresponding to multiple collection perspectives;

[0195] The data of the second difference indicator is compared with the data of the preset difference indicator to obtain the comparison result of the first indicator.

[0196] Based on the comparison results of the first indicator, the rendering detection results are generated.

[0197] In a specific embodiment, the multiple first difference index data corresponding to the multiple acquisition perspectives mentioned above may refer to the first difference index data included in the second color comparison results corresponding to each of the multiple acquisition perspectives.

[0198] In one specific embodiment, the second difference index data can be the largest first difference index data among multiple first difference index data corresponding to multiple collection perspectives.

[0199] In a specific embodiment, the largest first difference index data among the multiple first difference index data corresponding to the above multiple collection perspectives can be used as the second difference index data.

[0200] In one specific embodiment, the preset difference index data can be set according to actual application needs, and this disclosure does not limit it. For example, when the second difference index data is expressed in the form of percentage data, the preset difference index data can be 10%.

[0201] In one specific embodiment, the comparison result of the first indicator can be used to indicate the magnitude relationship between the second difference indicator data and the preset difference indicator data.

[0202] In a specific embodiment, generating rendering detection results based on the first indicator comparison result may include:

[0203] If the first indicator comparison result indicates that the second difference indicator data is greater than the preset difference indicator data, the first detection result will be used as the rendering detection result.

[0204] In a specific embodiment, if the first indicator comparison result indicates that the second difference indicator data is less than or equal to the preset difference indicator data, the second detection result can be used as the above-mentioned rendering detection result.

[0205] In one specific embodiment, the color difference analysis results corresponding to each acquisition viewpoint may include at least one fourth color comparison result. Each fourth color comparison result may include color difference index data. The color difference index data in each fourth color comparison result can be used to indicate the degree of color difference between the corresponding two model images.

[0206] In a specific embodiment, the aforementioned fourth color comparison result can be the aforementioned first color comparison result, or it can be the third color comparison result, or it can be the third color comparison result with the largest difference index data among at least one third color comparison result corresponding to any acquisition viewpoint. It is understood that the fourth color comparison result can be obtained by comparing every two model images in the image set to be compared, or it can be obtained by comparing the original position model image and any offset position model image in the image set to be compared.

[0207] In a specific embodiment, the above-mentioned generation of rendering detection results for the 3D model to be detected based on the color difference analysis results corresponding to each acquisition viewpoint may include:

[0208] Based on the color difference index data in multiple fourth color comparison results corresponding to multiple acquisition perspectives, the fifth color comparison result is determined.

[0209] The color difference index data in the fifth color comparison result is compared with the preset difference index data to obtain the second index comparison result.

[0210] Based on the comparison results of the second indicator, the rendering detection results are generated.

[0211] In one specific embodiment, the fifth color comparison result can be the comparison result with the largest color difference index data among multiple fourth color comparison results corresponding to multiple acquisition perspectives.

[0212] In one specific embodiment, the second index comparison result can be used to indicate the magnitude relationship between the color difference index data in the fifth color comparison result and the preset difference index data.

[0213] In a specific embodiment, if the second index comparison result indicates that the color difference index data in the fifth color comparison result is greater than the preset difference index data, the rendering detection result can be determined as the first detection result.

[0214] In a specific embodiment, if the second index comparison result indicates that the color difference index data in the fifth color comparison result is less than or equal to the preset difference index data, the rendering detection result can be determined as the second detection result.

[0215] In one specific embodiment, the above method may further include:

[0216] In response to the model search request, obtain multiple candidate 3D models and their respective rendering detection results;

[0217] Based on the rendering detection results, at least one 3D model to be displayed is determined from multiple candidate 3D models corresponding to the model search request.

[0218] In one specific embodiment, a model search request can be used to request the search for a desired 3D model from multiple preset 3D models. The model search request may include search feature description information. This search feature description information can be used to describe the features of the 3D model that the model search request expects to find.

[0219] In a specific embodiment, based on the search feature description information in the model search request, a preset 3D model whose corresponding features match the search feature description information can be selected from the above multiple preset 3D models as the above multiple candidate 3D models.

[0220] In a specific embodiment, each candidate 3D model can be detected based on the above steps S201 to S207 to obtain the rendering detection result corresponding to each candidate 3D model.

[0221] In one specific embodiment, at least one 3D model to be displayed can be used as a request return result of the aforementioned model search request. Specifically, the at least one 3D model to be displayed can be returned to the request terminal corresponding to the aforementioned model search request, and correspondingly, the request terminal can display the at least one 3D model to be displayed.

[0222] In one specific embodiment, the rendering detection result corresponding to at least one 3D model to be displayed can be a second detection result. The second detection result can be used to indicate that there is no depth conflict during the rendering process of the 3D model to be detected.

[0223] In a specific embodiment, the model recommendation index data corresponding to each candidate 3D model can be determined based on the rendering detection results corresponding to each candidate 3D model; correspondingly, at least one 3D model to be displayed can be selected from multiple candidate 3D models based on the model recommendation index data corresponding to each candidate 3D model.

[0224] In one specific embodiment, the model recommendation index data corresponding to any candidate 3D model can be used to indicate the quality of any candidate 3D model.

[0225] In a specific embodiment, if the rendering detection result corresponding to any candidate 3D model is the first detection result, the matching index data corresponding to any candidate 3D model can be updated to obtain model recommendation index data corresponding to any candidate 3D model. The matching index data corresponding to any candidate 3D model can be used to indicate the degree of matching between the candidate 3D model and the search feature description information. Specifically, data smaller than the matching index data corresponding to any candidate 3D model can be used as the model recommendation index data corresponding to any candidate 3D model.

[0226] In one specific embodiment, the candidate 3D models can be sorted according to the model recommendation index data corresponding to each candidate 3D model, in descending order of data, to obtain a candidate model sequence. A preset number of candidate 3D models from the candidate model sequence can be selected as at least one candidate 3D model to be displayed. Specifically, the preset number of displays can be set according to actual application needs, and this disclosure does not limit it.

[0227] In the above embodiments, in response to a model search request, multiple candidate 3D models and their respective rendering detection results are obtained. Based on the rendering detection results, at least one 3D model to be displayed corresponding to the model search request is determined from the multiple candidate 3D models. This can improve the quality of search results and thus improve the quality of application development.

[0228] In the above embodiments, by acquiring the 3D model to be detected, model image acquisition is performed on the 3D model to be detected, resulting in a set of comparison images corresponding to multiple acquisition viewpoints. The set of comparison images corresponding to each acquisition viewpoint includes the original position model image acquired at the original acquisition position corresponding to each acquisition viewpoint and the offset position model image acquired at at least one offset acquisition position corresponding to each acquisition viewpoint. The distance between at least one offset acquisition position corresponding to each acquisition viewpoint and the original acquisition position corresponding to each acquisition viewpoint is less than a preset distance. This enables the acquisition of model images of the 3D model to be detected at multiple acquisition positions under multiple acquisition viewpoints. Then, by combining the original position model image corresponding to each acquisition viewpoint and the offset position model image at at least one offset acquisition position corresponding to each acquisition viewpoint, color difference analysis is performed on each acquisition viewpoint to obtain the color difference analysis result corresponding to each acquisition viewpoint. This enables the color difference analysis between model images acquired at multiple acquisition positions under each acquisition viewpoint. Then, by combining the color difference analysis result corresponding to each acquisition viewpoint, the rendering detection result corresponding to the 3D model to be detected is generated. This enables the detection of whether there is depth conflict in the 3D model to be detected, which can improve the efficiency and accuracy of depth conflict detection.

[0229] Figure 7 This is a block diagram illustrating an anomaly detection device according to an exemplary embodiment. Specifically, as shown in... Figure 7 As shown, the device may include:

[0230] The model acquisition module 710 can be used to acquire the 3D model to be detected;

[0231] The model image acquisition module 720 can be used to acquire model images of the 3D model to be detected, and obtain a set of comparison images corresponding to multiple acquisition viewpoints. The set of comparison images corresponding to each acquisition viewpoint includes the original position model image acquired at the original acquisition position corresponding to each acquisition viewpoint and the offset position model image acquired at at least one offset acquisition position corresponding to each acquisition viewpoint. The distance between at least one offset acquisition position corresponding to each acquisition viewpoint and the original acquisition position corresponding to each acquisition viewpoint is less than a preset distance.

[0232] The color difference analysis module 730 can be used to perform color difference analysis on each acquisition viewpoint based on the original position model image corresponding to each acquisition viewpoint and the offset position model image of at least one offset acquisition position corresponding to each acquisition viewpoint, and obtain the color difference analysis result corresponding to each acquisition viewpoint.

[0233] The detection result generation module 740 can be used to generate the rendering detection result of the 3D model to be detected based on the color difference analysis result corresponding to each acquisition view. The rendering detection result is used to indicate whether there is a depth conflict during the rendering process of the 3D model to be detected.

[0234] In one specific embodiment, the above-described apparatus may further include:

[0235] The mesh color update module can be used to update the mesh colors of multiple meshes in the 3D model to be inspected based on multiple preset color information to obtain the updated 3D model to be inspected; the color difference between any two preset color information is greater than the preset color difference level.

[0236] Accordingly, the above-mentioned model image acquisition module 720 may include:

[0237] The first acquisition submodule can be used to acquire model images of the updated 3D model to be detected, and obtain a set of images to be compared.

[0238] In one specific embodiment, the above-mentioned grid color update module may include:

[0239] The current grid determination submodule can be used to determine the current grid from multiple grids; the current grid is any one of the multiple grids whose color configuration information has not been updated.

[0240] The color determination submodule can be used to determine the target color information from multiple preset color information, where the target color information is any one of the multiple preset color information;

[0241] The color configuration update submodule can be used to update the color configuration information corresponding to the current mesh in the 3D model to be detected based on the target color information, and repeat the mesh configuration update steps from multiple meshes to determine the current mesh and update the color configuration information corresponding to the current mesh in the 3D model to be detected based on the target color information, until the currently updated mesh in multiple meshes meets the preset convergence condition.

[0242] The model determination submodule can be used to take the 3D model to be detected that meets the preset convergence conditions as the updated 3D model to be detected.

[0243] In one specific embodiment, the color difference analysis module 730 described above may include:

[0244] The first analysis submodule can be used to perform color comparison analysis on the original position model image and the target comparison image corresponding to each acquisition viewpoint to obtain at least one first color comparison result for each acquisition viewpoint; the target comparison image corresponding to each acquisition viewpoint is any one of the offset position model images of at least one offset acquisition position corresponding to each acquisition viewpoint.

[0245] The first result generation submodule can be used to generate color difference analysis results for each acquisition viewpoint based on at least one first color comparison result corresponding to each acquisition viewpoint.

[0246] In one specific embodiment, the first result generation submodule described above may include:

[0247] The first result determination unit can be used to determine the second color comparison result corresponding to each acquisition viewpoint from at least one first color comparison result corresponding to each acquisition viewpoint; the second color comparison result corresponding to each acquisition viewpoint is the color comparison result indicating the largest color difference among at least one first color comparison result corresponding to each acquisition viewpoint.

[0248] The second result determination unit can be used to take the second color comparison result corresponding to each acquisition viewpoint as the color difference analysis result corresponding to each acquisition viewpoint.

[0249] In a specific embodiment, the second color comparison result corresponding to each acquisition viewpoint includes first difference index data, which is used to indicate the degree of color difference between the corresponding two model images; the second result determination unit may include:

[0250] The difference index determination subunit can be used to determine the second difference index data from multiple first difference index data corresponding to multiple acquisition perspectives; the second difference index data is the largest first difference index data among multiple first difference index data corresponding to multiple acquisition perspectives.

[0251] The comparison processing subunit can be used to compare the second difference index data with the preset difference index data to obtain the comparison result of the first index.

[0252] The result generation subunit can be used to generate rendering detection results based on the comparison results of the first indicator.

[0253] In one specific embodiment, the above result generation subunit may include:

[0254] The result determination subunit can be used to take the first detection result as the rendering detection result when the first indicator comparison result indicates that the second difference indicator data is greater than the preset difference indicator data; the first detection result is used to indicate that there is a depth conflict during the rendering process of the 3D model to be detected.

[0255] In one specific embodiment, the color difference analysis module 730 described above may include:

[0256] The image pair generation submodule can be used to generate at least one image pair to be compared for each acquisition viewpoint based on every two model images in the set of images to be compared for each acquisition viewpoint.

[0257] The second analysis submodule can be used to perform color comparison analysis on the two model images in each image pair to be compared, and obtain the third color comparison result corresponding to each image pair to be compared.

[0258] The second result generation submodule can be used to generate color difference analysis results for each acquisition viewpoint based on at least one third color comparison result corresponding to each acquisition viewpoint.

[0259] In a specific embodiment, the color difference analysis result corresponding to each acquisition viewpoint includes at least one fourth color comparison result, each fourth color comparison result includes color difference index data, and the color difference index data in each fourth color comparison result is used to indicate the degree of color difference between the corresponding two model images; the detection result generation module 740 may include:

[0260] The result determination submodule can be used to determine the fifth color comparison result based on the color difference index data in the multiple fourth color comparison results corresponding to multiple acquisition perspectives; the fifth color comparison result is the comparison result with the largest corresponding color difference index data in the multiple fourth color comparison results corresponding to multiple acquisition perspectives.

[0261] The comparison processing submodule can be used to compare the color difference index data in the fifth color comparison result with the preset difference index data to obtain the second index comparison result.

[0262] The third result generation submodule can be used to generate rendering detection results based on the comparison results of the second indicator.

[0263] In one specific embodiment, the above-described apparatus may further include:

[0264] The location information acquisition module can be used to acquire the original acquisition location information corresponding to the original acquisition location of multiple acquisition viewpoints; the original acquisition location information corresponding to each acquisition viewpoint is used to characterize the relative position of the original acquisition location of each acquisition viewpoint with respect to the origin position of the 3D model to be detected.

[0265] The location information determination module can be used to determine at least one offset acquisition location information corresponding to each acquisition viewpoint based on the original acquisition location information corresponding to the original acquisition positions of multiple acquisition viewpoints; the at least one offset acquisition location information is the location information of at least one offset acquisition position corresponding to each acquisition viewpoint.

[0266] Accordingly, the above-mentioned model image acquisition module 720 may include:

[0267] The second acquisition submodule can be used to acquire model images of the 3D model to be detected based on at least one offset acquisition position information corresponding to each acquisition viewpoint and the original acquisition position information corresponding to each acquisition viewpoint, so as to obtain a set of images to be compared.

[0268] In one specific embodiment, the location information determination module described above may include:

[0269] The acquisition distance determination submodule can be used to determine the target acquisition distance for each acquisition viewpoint based on the original acquisition position information corresponding to each acquisition viewpoint; the target acquisition distance for each acquisition viewpoint is the distance between the original acquisition position of each acquisition viewpoint and the origin position of the 3D model to be detected.

[0270] The acquisition plane determination submodule can be used to determine the target acquisition plane corresponding to each acquisition viewpoint based on the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected. The target acquisition plane is a plane perpendicular to the target acquisition line corresponding to each acquisition viewpoint, and the original acquisition position corresponding to each acquisition viewpoint belongs to the target acquisition plane. The target acquisition line corresponding to each acquisition viewpoint is the line connecting the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected.

[0271] The region radius determination submodule can be used to determine the offset region radius corresponding to each acquisition viewpoint based on the target acquisition distance corresponding to each acquisition viewpoint; the offset region radius corresponding to each acquisition viewpoint is smaller than the target acquisition distance corresponding to each acquisition viewpoint.

[0272] The planar region determination submodule can be used to determine the target acquisition plane region for each acquisition viewpoint based on the offset region radius and the target acquisition plane for each acquisition viewpoint. The target acquisition plane region is a circular region in the target acquisition plane for each acquisition viewpoint, with the original acquisition position for each acquisition viewpoint as the origin and the offset region radius for each acquisition viewpoint as the radius.

[0273] The location information determination submodule can be used to determine the offset acquisition position information of at least one offset acquisition position corresponding to each acquisition view based on the target acquisition plane area corresponding to each acquisition view; at least one offset acquisition position corresponding to each acquisition view is located within the target acquisition plane area corresponding to each acquisition view.

[0274] In one specific embodiment, the above-described apparatus may further include:

[0275] The detection result acquisition module can be used to respond to model search requests and acquire multiple candidate 3D models and their respective rendering detection results;

[0276] The model filtering module can be used to determine at least one 3D model to be displayed from multiple candidate 3D models based on the rendering detection results; the rendering detection result corresponding to at least one 3D model to be displayed is the second detection result, which is used to indicate that there is no depth conflict during the rendering process of the 3D model to be detected.

[0277] Regarding the apparatus in the above embodiments, the specific manner in which each module and unit performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0278] Figure 8This is a block diagram illustrating an electronic device for implementing anomaly detection on a 3D model to be inspected, according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned anomaly detection method.

[0279] Figure 9 This is a block diagram illustrating another electronic device for implementing anomaly detection on a 3D model to be inspected, according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned anomaly detection method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0280] Those skilled in the art will understand that Figure 8 or Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the electronic device to which the present disclosure is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0281] In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the anomaly detection method as described in the embodiments of this disclosure.

[0282] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the anomaly detection method in the embodiments of this disclosure.

[0283] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the anomaly detection method in the embodiments of this disclosure.

[0284] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0285] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0286] It is understood that in the specific embodiments of this application, data such as user information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0287] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0288] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An anomaly detection method, characterized in that, The method includes: Obtain the 3D model to be inspected; The three-dimensional model to be detected is image acquired to obtain a set of comparison images corresponding to multiple acquisition viewpoints. The set of comparison images corresponding to each acquisition viewpoint includes the original position model image acquired at the original acquisition position corresponding to each acquisition viewpoint and the offset position model image acquired at at least one offset acquisition position corresponding to each acquisition viewpoint. The distance between at least one offset acquisition position corresponding to each acquisition viewpoint and the original acquisition position corresponding to each acquisition viewpoint is less than a preset distance. Based on the original position model image corresponding to each acquisition viewpoint and the offset position model image of at least one offset acquisition position corresponding to each acquisition viewpoint, color difference analysis is performed on each acquisition viewpoint to obtain the color difference analysis result corresponding to each acquisition viewpoint. Based on the color difference analysis results corresponding to each acquisition viewpoint, a rendering detection result is generated for the 3D model to be detected. The rendering detection result is used to indicate whether there is a depth conflict during the rendering process of the 3D model to be detected.

2. The method according to claim 1, characterized in that, The method further includes: Based on multiple preset color information, the mesh colors of multiple meshes in the 3D model to be detected are updated to obtain the updated 3D model to be detected; the color difference between any two preset color information is greater than the preset color difference. The process involves acquiring model images of the 3D model to be detected, resulting in multiple sets of comparison images corresponding to different acquisition perspectives, including: The updated 3D model to be detected is subjected to model image acquisition to obtain the set of images to be compared.

3. The method according to claim 2, characterized in that, The step of updating the mesh colors of multiple meshes in the 3D model to be detected based on multiple preset color information to obtain an updated 3D model to be detected includes: The current grid is determined from the plurality of grids; the current grid is any one of the plurality of grids whose color configuration information has not been updated. From the plurality of preset color information, a target color information is determined, wherein the target color information is any one of the plurality of preset color information; Based on the target color information, update the color configuration information corresponding to the current mesh in the 3D model to be detected, and repeat the mesh configuration update steps from determining the current mesh from the multiple meshes to updating the color configuration information corresponding to the current mesh in the 3D model to be detected based on the target color information, until the currently updated mesh among the multiple meshes meets the preset convergence condition; The 3D model to be detected that meets the preset convergence condition is used as the updated 3D model to be detected.

4. The method according to claim 1, characterized in that, The step involves performing color difference analysis on each acquisition viewpoint based on the original position model image corresponding to each acquisition viewpoint and the offset position model image of at least one offset acquisition position corresponding to each acquisition viewpoint, to obtain the color difference analysis results for each acquisition viewpoint, including: Color comparison analysis is performed on the original position model image and the target comparison image corresponding to each acquisition viewpoint to obtain at least one first color comparison result for each acquisition viewpoint; the target comparison image corresponding to each acquisition viewpoint is any one of the offset position model images of at least one offset acquisition position corresponding to each acquisition viewpoint. Based on at least one first color comparison result corresponding to each acquisition viewpoint, a color difference analysis result corresponding to each acquisition viewpoint is generated.

5. The method according to claim 4, characterized in that, The step of generating color difference analysis results for each acquisition viewpoint based on at least one first color comparison result for each acquisition viewpoint includes: From at least one first color comparison result corresponding to each acquisition viewpoint, a second color comparison result corresponding to each acquisition viewpoint is determined; the second color comparison result corresponding to each acquisition viewpoint is the color comparison result indicating the largest color difference among at least one first color comparison result corresponding to each acquisition viewpoint. The second color comparison result corresponding to each acquisition viewpoint is used as the color difference analysis result corresponding to each acquisition viewpoint.

6. The method according to claim 5, characterized in that, The second color comparison result corresponding to each acquisition viewpoint includes first difference index data, which is used to indicate the degree of color difference between the two corresponding model images; the step of generating the rendering detection result corresponding to the 3D model to be detected based on the color difference analysis result corresponding to each acquisition viewpoint includes: From the multiple first difference index data corresponding to the multiple acquisition perspectives, a second difference index data is determined; the second difference index data is the largest first difference index data among the multiple first difference index data corresponding to the multiple acquisition perspectives. The second difference index data is compared with the preset difference index data to obtain the first index comparison result; Based on the comparison results of the first indicator, the rendering detection results are generated.

7. The method according to claim 6, characterized in that, The step of generating the rendering detection result based on the comparison result of the first indicator includes: If the first indicator comparison result indicates that the second difference indicator data is greater than the preset difference indicator data, the first detection result is used as the rendering detection result; the first detection result is used to indicate that there is a depth conflict during the rendering process of the 3D model to be detected.

8. The method according to claim 1, characterized in that, The step involves performing color difference analysis on each acquisition viewpoint based on the original position model image corresponding to each acquisition viewpoint and the offset position model image of at least one offset acquisition position corresponding to each acquisition viewpoint, to obtain the color difference analysis results for each acquisition viewpoint, including: Based on every two model images in the set of images to be compared corresponding to each acquisition viewpoint, at least one pair of images to be compared corresponding to each acquisition viewpoint is generated. Color comparison analysis is performed on the two model images in each pair of images to be compared to obtain the third color comparison result corresponding to each pair of images to be compared. Based on at least one third color comparison result corresponding to each acquisition viewpoint, a color difference analysis result corresponding to each acquisition viewpoint is generated.

9. The method according to claim 1, characterized in that, The color difference analysis results corresponding to each acquisition viewpoint include at least one fourth color comparison result, and each fourth color comparison result includes color difference index data. The color difference index data in each fourth color comparison result is used to indicate the degree of color difference between the corresponding two model images. The step of generating rendering detection results corresponding to the 3D model to be detected based on the color difference analysis results corresponding to each acquisition viewpoint includes: Based on the color difference index data in the multiple fourth color comparison results corresponding to the multiple acquisition perspectives, a fifth color comparison result is determined; the fifth color comparison result is the comparison result with the largest corresponding color difference index data in the multiple fourth color comparison results corresponding to the multiple acquisition perspectives. The color difference index data in the fifth color comparison result is compared with the preset difference index data to obtain the second index comparison result. Based on the comparison results of the second indicator, the rendering detection results are generated.

10. The method according to claim 1, characterized in that, The method further includes: Obtain the original acquisition position information corresponding to each of the multiple acquisition viewpoints; the original acquisition position information corresponding to each acquisition viewpoint is used to characterize the relative position of the original acquisition position corresponding to each acquisition viewpoint with respect to the origin position of the three-dimensional model to be detected; Based on the original acquisition position information corresponding to the original acquisition positions of the multiple acquisition viewpoints, at least one offset acquisition position information corresponding to each acquisition viewpoint is determined; the at least one offset acquisition position information is the position information of at least one offset acquisition position corresponding to each acquisition viewpoint. The process involves acquiring model images of the 3D model to be detected, resulting in multiple sets of comparison images corresponding to different acquisition perspectives, including: Based on at least one offset acquisition position information corresponding to each acquisition viewpoint and the original acquisition position information corresponding to each acquisition viewpoint, model image acquisition is performed on the three-dimensional model to be detected to obtain the set of images to be compared.

11. The method according to claim 10, characterized in that, The determination of at least one offset acquisition position information corresponding to each acquisition viewpoint based on the original acquisition position information corresponding to the original acquisition positions of the multiple acquisition viewpoints includes: Based on the original acquisition position information corresponding to each acquisition viewpoint, the target acquisition distance corresponding to each acquisition viewpoint is determined; the target acquisition distance corresponding to each acquisition viewpoint is the distance between the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected. Based on the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected, the target acquisition plane corresponding to each acquisition viewpoint is determined; the target acquisition plane is a plane perpendicular to the target acquisition line corresponding to each acquisition viewpoint, and the original acquisition position corresponding to each acquisition viewpoint belongs to the target acquisition plane, and the target acquisition line corresponding to each acquisition viewpoint is the line connecting the original acquisition position corresponding to each acquisition viewpoint and the origin position of the 3D model to be detected; Based on the target acquisition distance corresponding to each acquisition viewpoint, the radius of the offset region corresponding to each acquisition viewpoint is determined; the radius of the offset region corresponding to each acquisition viewpoint is smaller than the target acquisition distance corresponding to each acquisition viewpoint. Based on the offset region radius corresponding to each acquisition viewpoint and the target acquisition plane corresponding to each acquisition viewpoint, the target acquisition plane region corresponding to each acquisition viewpoint is determined; the target acquisition plane region is a circular region in the target acquisition plane corresponding to each acquisition viewpoint with the original acquisition position corresponding to each acquisition viewpoint as the origin and the offset region radius corresponding to each acquisition viewpoint as the radius; Based on the target acquisition plane area corresponding to each acquisition viewpoint, the offset acquisition position information of at least one offset acquisition position corresponding to each acquisition viewpoint is determined; the at least one offset acquisition position corresponding to each acquisition viewpoint is located within the target acquisition plane area corresponding to each acquisition viewpoint.

12. The method according to any one of claims 1-11, characterized in that, The method further includes: In response to a model search request, obtain multiple candidate 3D models and the rendering detection results corresponding to each of the multiple candidate 3D models; Based on the rendering detection results, at least one 3D model to be displayed corresponding to the model search request is determined from the plurality of candidate 3D models; the rendering detection result corresponding to the at least one 3D model to be displayed is the second detection result, which is used to indicate that there is no depth conflict during the rendering process of the 3D model to be detected.

13. An anomaly detection device, characterized in that, The device includes: The model acquisition module is used to acquire the 3D model to be detected. The model image acquisition module is used to acquire model images of the 3D model to be detected, and obtain a set of comparison images corresponding to multiple acquisition viewpoints; the set of comparison images corresponding to each acquisition viewpoint includes the original position model image acquired at the original acquisition position corresponding to each acquisition viewpoint and the offset position model image acquired at at least one offset acquisition position corresponding to each acquisition viewpoint; the distance between at least one offset acquisition position corresponding to each acquisition viewpoint and the original acquisition position corresponding to each acquisition viewpoint is less than a preset distance; The color difference analysis module is used to perform color difference analysis on each acquisition viewpoint based on the original position model image corresponding to each acquisition viewpoint and the offset position model image of at least one offset acquisition position corresponding to each acquisition viewpoint, and obtain the color difference analysis result corresponding to each acquisition viewpoint. The detection result generation module is used to generate the rendering detection result corresponding to the three-dimensional model to be detected based on the color difference analysis result corresponding to each acquisition viewpoint. The rendering detection result is used to indicate whether there is a depth conflict during the rendering process of the three-dimensional model to be detected.

14. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the executable instructions to implement the anomaly detection method according to any one of claims 1 to 12.

15. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the anomaly detection method according to any one of claims 1 to 12.

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