A Selective 3D Rendering Method Based on 3D Scenes

Abstract

The present invention discloses a selective 3D rendering method based on a three-dimensional scene, specifically relating to the technical field of 3D rendering. It includes determining similar users through the historical data of users, determining the evaluation information of users on different perfume components, determining the relevance information of users on different perfume component styles according to the positions of perfume component tags in the knowledge graph, determining the recommended styles of different components before the rendering process of the user-defined perfume rendering scheme, and loading the 3D model data of the recommended styles in the background. According to the user-defined perfume rendering scheme, the longest folding edge of the perfume rendering scheme at different rotation angles is determined through discretization analysis to determine the rendering quality information of the perfume rendering scheme, and the matching degree information of the perfume rendering scheme is determined by determining the curvature feature changes and geometric shape changes of each special marker point on the grid. The present invention helps to improve the quality of user-defined perfume design and the interactive experience.

Description

Technical Field

[0001] The present invention relates to the field of 3D rendering technology, and more specifically, to a selective 3D rendering method based on a three-dimensional scene. Background Art

[0002] In the fields of personalized product design and 3D visualization, the user demand for customized perfume bottle design is increasing. Modern 3D rendering systems usually allow users to freely select components such as bottle bodies, nozzles, bottle caps, and middle covers, and provide interactive 3D previews. However, existing systems usually rely on fixed templates or manual screening methods. The traditional 3D rendering process needs to load the 3D model data of all components in real time, resulting in a large amount of calculation and easy to cause delays. Especially in complex rendering scenarios, it affects the smoothness. Moreover, the shape matching relationship of components before and after assembly in the user-defined 3D design scheme may not be effectively modeled, resulting in visual or structural disharmony after some component combinations, affecting the overall aesthetics, and it is difficult for users to intuitively understand the final presentation effect of their customized solutions.

[0003] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0004] In order to overcome the above defects of the prior art, an embodiment of the present invention provides a selective 3D rendering method based on a three-dimensional scene to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A selective 3D rendering method based on a three-dimensional scene specifically includes the following steps:

[0007] S1: Determine similar users through the historical data of the user, determine the evaluation information of the user on different perfume components, and determine the relevance information of different perfume component styles of the user according to the positions of perfume component tags in the knowledge graph;

[0008] S2: Before the rendering process of the user-defined perfume rendering scheme, determine the recommended styles of different components and load the 3D model data of the recommended styles in the background;

[0009] S3: According to the user-defined perfume rendering scheme, determine the rendering quality information of the perfume rendering scheme by discretization analysis of the longest folding edge of the perfume rendering scheme at different rotation angles;

[0010] S4: Determine the change in the curvature feature of each special marked point on the grid through the special marked points before and after the combined rendering of the components in the perfume rendering scheme, and determine the matching degree information of the perfume rendering scheme by measuring the geometric shape change with the Hausdorff distance;

[0011] S5: Comprehensively analyze the rendering quality information and matching degree information of the perfume rendering scheme to determine the rendering performance of the user's perfume rendering scheme in the 3D scene.

[0012] In a preferred embodiment, determine the evaluation information and relevance information of the user for different perfume components, including:

[0013] Collect the historical data of the user's selected perfume component styles. By collaborative filtering of the historical data, find other users similar to the user, and based on the similar users, predict the degree of interest of the target user in the component styles that have not been rated through a weighted average method, and determine the evaluation information of different perfume component styles, and represent the evaluation information of different perfume component styles through the interest prediction value coefficient;

[0014] According to the historical data of the user's selected perfume component styles, determine the labels of the perfume group styles selected by the user. According to the positions of the labels of the perfume group styles in the knowledge graph, determine the degree of interest of the user in other component styles, determine the relevance information of different perfume component styles, and represent the relevance information of different perfume component styles through the graph connection distance coefficient.

[0015] In a preferred embodiment, the acquisition logic of the interest prediction value coefficient is as follows:

[0016] According to the historical data of the user's selected perfume component styles, obtain the scores of the perfume component styles selected by the user in history, and mark the scores of the perfume component styles selected by the user in history as: , determine other users who have selected the same perfume component styles as the user, and mark the scores of the perfume component styles selected by other users as: , where i = 1, 2, 3,..., I, I is a positive integer, i is the number of the perfume component styles selected by the user in history, n = 1, 2, 3,..., N, N is a positive integer, and n is the number of other users who have selected the same perfume component styles as the user;

[0017] Determine the similarity of user behavior through cosine similarity, set a similarity threshold to screen similar users, and the calculation formula is: ; where is the similarity between the nth other user and the user;

[0018] Obtain users whose similarity between other users and the user is greater than the similarity threshold, and record the users whose similarity between other users and the user is greater than the similarity threshold as similar users. Calculate the interest prediction value coefficient of the user in other component styles through weighted average, and the calculation formula is: ; where is the interest prediction value coefficient of the kth component style.

[0019] In a preferred embodiment, the acquisition logic of the map connection distance coefficient is as follows:

[0020] According to the historical data of the user's selected perfume component styles, obtain the labels of the user's historical selected perfume component styles. Use the labels of the perfume component styles as the parent nodes in the knowledge graph. Different perfume component styles belong to the child nodes under the parent nodes. The nodes are connected by edges, and the length of the edges represents the strength of the relationship.

[0021] According to the scores of the user's historical selected perfume component styles, determine the total scores of different parent nodes. The calculation formula is: , where is the total score of the user for different parent nodes, is the score of the user's q-th perfume component style under the m-th parent node;

[0022] Determine the map connection distance coefficients of different perfume component styles. The calculation formula is: ; where is the map connection distance coefficient of the k-th perfume component style, is the relationship weight of the k-th perfume component style with the parent node, and JD is the total score of the parent node of the k-th perfume component style.

[0023] In a preferred embodiment, determining the recommended styles of different components includes:

[0024] Comprehensively analyze the evaluation information and relevance information of different perfume component styles. By weighted calculation of the interest prediction value coefficient and the map connection distance coefficient, determine the recommended evaluation coefficient of different perfume component styles. The calculation formula of the recommended evaluation coefficient is: ; where is the recommended evaluation coefficient, , are respectively the proportionality coefficients of the interest prediction value coefficient and the map connection distance coefficient, , are both greater than 0;

[0025] Set the recommended evaluation coefficient threshold. Compare the recommended evaluation coefficients of different perfume component styles with the recommended evaluation coefficient threshold. If the recommended evaluation coefficient of a different perfume component style is greater than the recommended evaluation coefficient threshold, then load the perfume component style before the user customizes the perfume rendering scheme. If the recommended evaluation coefficient of a different perfume component style is less than the recommended evaluation coefficient threshold, then do not perform any operation on the perfume component style.

[0026] In a preferred embodiment, determining the rendering quality information and matching degree information of the perfume rendering scheme includes:

[0027] By selecting different perfume component styles, the user constitutes a custom perfume rendering scheme. By comparing the rendering effects of each perfume component in the custom perfume rendering scheme before and after combination, the rendering quality information and matching degree information of the perfume rendering scheme are determined. The rendering quality information of the perfume rendering scheme is represented by the screen space error accumulation coefficient, and the matching degree information of the perfume rendering scheme is represented by the curvature change coefficient and the geometric variation measurement coefficient.

[0028] In a preferred embodiment, the acquisition logic of the screen space error accumulation coefficient is as follows:

[0029] During the rendering of the user-defined perfume rendering scheme, by discretizing and analyzing the longest folding edge of the perfume rendering scheme at different rotation angles, the geometric error based on the longest folding edge is determined. The geometric errors of the longest folding edge at different rotation angles with the x-axis as the rotation axis are marked as: The geometric errors of the longest folding edge at different rotation angles with the y-axis as the rotation axis are marked as: The geometric errors of the longest folding edge at different rotation angles with the z-axis as the rotation axis are marked as: where J is a positive integer, 、 、 are the numbers of the rotation angles of the x-axis, y-axis, and z-axis respectively;

[0030] Determine the height of the perfume on the screen in the custom perfume rendering scheme, and mark the heights of the perfume on the screen at different rotation angles of the x-axis, y-axis, and z-axis as: 、 and ;

[0031] Determine the distance between the perfume and the camera in the custom perfume rendering scheme, and mark the distances between the perfume and the camera at different rotation angles of the x-axis, y-axis, and z-axis as: 、 and ;

[0032] Determine the field of view angle of the camera, and mark the field of view angle of the camera as: FOV, and calculate the screen space error accumulation coefficient. The calculation formula is:

[0033] ; where is the screen space error accumulation coefficient.

[0034] In a preferred embodiment, the acquisition logic of the curvature change coefficient is as follows:

[0035] Determine the special marking points for each style of the perfume components, construct a discrete grid of the perfume components, determine the LBO value of the special marking points in the user-defined perfume rendering scheme before component combination, and mark the LBO value of the special marking points in the user-defined perfume rendering scheme before component combination as: , and mark the LBO value of the special marking points in the user-defined perfume rendering scheme after component combination as: , where g = 1, 2, 3, ……, G, G is a positive integer, and g is the number of the special marking points;

[0036] Calculate the curvature change coefficient, and the calculation formula is: ; where is the curvature change coefficient, f(*) is the Laplace-Beltrami operator, is the L1 norm.

[0037] In a preferred embodiment, the acquisition logic of the geometric variation metric coefficient is:

[0038] Determine the vertex set of the three-dimensional models of adjacent perfume components after component combination in the user-defined perfume rendering scheme, mark the point sets of two adjacent components as A and B, and measure the maximum and minimum distances between the point sets of two adjacent components through the Hausdorff distance. The calculation formula is: ; is the Hausdorff distance between two adjacent components, is the minimum distance from the point set in set A to set B, is the minimum distance from the point set in set B to set A;

[0039] Obtain the Hausdorff distances of different adjacent perfume components in the user-defined perfume rendering scheme, calculate the geometric variation metric coefficient, and the calculation formula is: ; where w = 1, 2, 3, ……, L, L is a positive integer, and w is the number of different adjacent perfume components, is the geometric variation metric coefficient.

[0040] In a preferred embodiment, determine the performance of the user's perfume rendering scheme in the three-dimensional scene, including:

[0041] Comprehensively analyze the rendering quality information and matching degree information of the perfume rendering scheme, perform weighted calculation through the screen space error accumulation coefficient, the curvature change coefficient, and the geometric variation metric coefficient, construct a perfume rendering evaluation model, generate a perfume rendering evaluation coefficient, and the calculation formula of the perfume rendering evaluation coefficient is: ; where is the perfume rendering evaluation coefficient, , , They are the proportionality coefficients of the screen space error accumulation coefficient, the curvature change amount coefficient, and the geometric variation measurement coefficient respectively. , , All are greater than 0 respectively.

[0042] Set the perfume rendering evaluation coefficient threshold, compare the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme with the perfume rendering evaluation coefficient threshold. If the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme is less than the perfume rendering evaluation coefficient threshold, a warning signal is generated.

[0043] If the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme is greater than the perfume rendering evaluation coefficient threshold, no warning signal is generated.

[0044] The technical effects and advantages of the present invention:

[0045] Based on the user's historical data, knowledge graph, and 3D rendering analysis, the present invention optimizes the recommendation and rendering quality of the user-defined perfume rendering scheme. By using the user's historical data and knowledge graph, similar users are determined, the user's preferences for different perfume components are predicted, and the correlation between the perfume component styles is established. Before the user performs 3D rendering, the perfume component styles that meet the user's preferences are recommended, and the corresponding 3D models are pre-loaded to improve the rendering efficiency. Through discrete analysis, the geometric error of the perfume components at different rotation angles is evaluated, the longest folding edge is judged, the overall rendering quality is determined, and the matching degree between the overall components is determined. The performance of the user-defined perfume rendering scheme in the 3D scene is comprehensively evaluated. The present invention helps to improve the quality and interaction experience of user-defined perfume design, while reducing the rendering calculation burden and improving the system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 It is a schematic flow chart of a selective 3D rendering method based on a three-dimensional scene of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Embodiment 1

[0050] Figure 1 It is a schematic flow diagram of a selective 3D rendering method based on a three-dimensional scene, specifically including the following steps:

[0051] S1: Determine similar users through the historical data of users, determine the evaluation information of users on different perfume components, and determine the correlation information of different perfume component styles of users according to the positions of perfume component labels in the knowledge graph;

[0052] S2: Before the rendering process of the user-defined perfume rendering scheme, determine the recommended styles of different components, and load the 3D model data of the recommended styles in the background;

[0053] S3: According to the user-defined perfume rendering scheme, determine the rendering quality information of the perfume rendering scheme by discretizing and analyzing the longest folding edge of the perfume rendering scheme at different rotation angles;

[0054] S4: Determine the change in the curvature characteristics of each special marked point on the grid through the special marked points before and after the combined rendering of components in the perfume rendering scheme, and determine the matching degree information of the perfume rendering scheme by measuring the geometric shape change with the Hausdorff distance;

[0055] S5: Comprehensively analyze the rendering quality information and the matching degree information of the perfume rendering scheme to determine the performance of the user's perfume rendering scheme in the three-dimensional scene during rendering.

[0056] During the design process of the custom perfume scheme, the perfume is also composed of multiple components. The component types include the bottle body, the nozzle, the bottle cap, and the middle cover. Different component types include multiple styles. Through the combination of different styles in different component types, the user-defined perfume rendering scheme is determined.

[0057] Specifically, the user selects the styles of different components according to their own needs. The rendering content and styles of each component will be different. Different components may have different levels of detail or complexity. Therefore, only the part selected by the current user will be loaded and displayed during rendering;

[0058] For the selection of different styles, the user can dynamically adjust their material and color attributes, rather than statically rendering every possible combination, including the dynamic adjustment rendering of materials and colors, that is, rendering the corresponding appearance according to the user's selection.

[0059] Collect historical data on the perfume component styles selected by users. By collaborative filtering of the historical data, find other users similar to the user, and based on the similar users, use the weighted average method to predict the degree to which the target user may be interested in the component styles that have not been rated, determine the evaluation information of different perfume component styles, represent the evaluation information of different perfume component styles through the interest prediction value coefficient, determine the tags of the perfume component styles selected by the user according to the historical data of the user's selection of perfume component styles, determine the degree to which the user may be interested in other component styles according to the position of the tags of the perfume component styles in the knowledge graph, determine the relevance information of different perfume component styles, and represent the relevance information of different perfume component styles through the graph connection distance coefficient.

[0060] The acquisition logic of the interest prediction value coefficient is as follows: According to the historical data of the user's selection of perfume component styles, obtain the ratings of the perfume component styles selected by the user in the past, and mark the ratings of the perfume component styles selected by the user in the past as: , determine other users who have selected the same perfume component styles as the user, and mark the ratings of the perfume component styles selected by the other users as: , where i = 1, 2, 3,..., I, I is a positive integer, i is the number of the perfume component styles selected by the user in the past, n = 1, 2, 3,..., N, N is a positive integer, and n is the number of other users who have selected the same perfume component styles as the user;

[0061] It should be noted that the common point between the user and other users who have selected the same perfume component styles as the user is that they have used the same perfume component styles in their historical usage records. It should be noted that other users who have selected the same perfume component styles as the user have not only used the same perfume component styles as the user.

[0062] Determine the similarity of user behavior through cosine similarity, set a similarity threshold to screen similar users, and the calculation formula is: ; where is the similarity between the nth other user and the user;

[0063] Obtain users whose similarity between other users and the user is greater than the similarity threshold, and record the users whose similarity between other users and the user is greater than the similarity threshold as similar users. Calculate the interest prediction value coefficient of the user for other component styles through weighted average, and the calculation formula is: ; where is the interest prediction value coefficient of the kth component style.

[0064] It can be seen from the formula that the larger the interest prediction value coefficient of the component style, the more likely the user is to be interested in this component style. Before the user customizes the perfume rendering scheme, this component style can be loaded to reduce the burden of rendering calculation.

[0065] The acquisition logic of the graph connection distance coefficient is as follows: According to the historical data of the user's selected perfume component styles, obtain the labels of the user's historical selected perfume component styles. Use the labels of the perfume component styles as the parent nodes in the knowledge graph. Different perfume component styles belong to the child nodes under the parent node, and the nodes are connected by edges. The length of the edge represents the strength of the relationship.

[0066] It should be noted that the labels of the perfume component styles include fragrance type labels such as floral fragrance, fruity fragrance, and woody fragrance, design style labels such as modern minimalist, classic, and luxurious, and functional labels such as durability, leak-proof design, and easy to clean. There may be the same child nodes for different parent nodes, and the edges between the parent node and the child nodes are represented by relationship weights.

[0067] Determine the total score of different parent nodes according to the scores of the user's historical selected perfume component styles. The calculation formula is: , where is the total score of the user for different parent nodes, is the score of the user's q-th perfume component style under the m-th parent node;

[0068] It should be noted that the total score of different nodes represents the user's preference for different labels. The larger the total score of different nodes, the more interested the user is in the perfume component styles under that label.

[0069] Determine the graph connection distance coefficient of different perfume component styles. The calculation formula is: ; where is the graph connection distance coefficient of the k-th perfume component style, is the relationship weight between the k-th perfume component style and the parent node, and JD is the total score of the parent node of the k-th perfume component style.

[0070] It should be noted that the larger the graph connection distance coefficient, the more likely the perfume component style is to meet the user's selection. It can load this component style before the user customizes the perfume rendering scheme to reduce the burden of rendering calculation.

[0071] Comprehensively analyze the evaluation information and correlation information of different perfume component styles. Through the weighted calculation of the interest prediction value coefficient and the graph connection distance coefficient, determine the recommended evaluation coefficient of different perfume component styles. The calculation formula of the recommended evaluation coefficient is: ; where is the recommended evaluation coefficient, , are the proportionality coefficients of the interest prediction value coefficient and the graph connection distance coefficient respectively, , are both greater than 0.

[0072] Set a recommended evaluation coefficient threshold, compare the recommended evaluation coefficients of different perfume component styles with the recommended evaluation coefficient threshold. If the recommended evaluation coefficient of a different perfume component style is greater than the recommended evaluation coefficient threshold, then load the perfume component style before the user customizes the perfume rendering scheme, indicating that the perfume component style may be applied by the user, and it is necessary to pre-load the 3D model data of the perfume component style in advance. If the recommended evaluation coefficient of a different perfume component style is less than the recommended evaluation coefficient threshold, then no operation is performed on the perfume component style.

[0073] The user forms a custom perfume rendering scheme by selecting different perfume component styles. By comparing the rendering effect before and after combining each component of the perfume in the custom perfume rendering scheme, the rendering quality information and matching degree information of the perfume rendering scheme are determined. The rendering quality information of the perfume rendering scheme is represented by the screen space error accumulation coefficient, and the matching degree information of the perfume rendering scheme is represented by the curvature change coefficient and the geometric variation measurement coefficient.

[0074] The acquisition logic of the screen space error accumulation coefficient is as follows: During the rendering of the user-defined perfume rendering scheme, the longest folding edge of the perfume rendering scheme at different rotation angles is determined through discretization analysis, and the geometric error based on the longest folding edge is determined. The geometric error of the longest folding edge at different rotation angles with the x-axis as the rotation axis is marked as: The geometric error of the longest folding edge at different rotation angles with the y-axis as the rotation axis is marked as: The geometric error of the longest folding edge at different rotation angles with the z-axis as the rotation axis is marked as: where J is a positive integer, 、 、 are the numbers of the rotation angles with the x-axis, y-axis, and z-axis respectively;

[0075] It should be noted that when folding an edge, a certain error will be generated after the two endpoints are merged. This error is usually evaluated by calculating the displacement between the vertices before and after the merge. The size of the error will affect the accuracy of the simplified model. During the simplification process, usually the longest edge is selected for folding because folding a long edge usually generates a larger geometric error, thus affecting the appearance of the model;

[0076] In 3D calculations, the geometric information of an object is usually represented by a set of vertices, edges, and faces. To improve the rendering performance, it is often necessary to simplify these models, reducing the number of vertices or faces. The simplified model is not exactly equal to the original model and may lose some details. This distortion or deviation is the geometric error;

[0077] Determining the geometric error of the longest folding edge at different rotation angles with the x, y, and z axes as the rotation axes can optimize the accuracy of 3D model simplification or model rendering, ensure the visual quality from different perspectives, and by discretizing the rotation angles and discretizing the angles for each rotation axis, it is ensured that the computational load can be reduced and a reasonable result can be provided in a short time. The discretized rotation angles are set by the staff in the professional field.

[0078] Determine the height of the perfume on the screen in the custom perfume rendering scheme, and mark the heights of the perfume on the screen at different rotation angles around the x-axis, y-axis, and z-axis respectively as: 、 and ;

[0079] It should be noted that the height of the perfume on the screen refers to the projected height of the object after rendering. In a 3D scene, the perfume is transformed into the screen coordinate system through a projection matrix and then mapped to the pixel area on the screen. When the perfume rotates, the projected height of the perfume on the screen may change, especially when the rotation angle of the perfume causes changes in its shape and contour.

[0080] Determine the distance between the perfume and the camera in the custom perfume rendering scheme, and mark the distances between the perfume and the camera at different rotation angles around the x-axis, y-axis, and z-axis respectively as: 、 and ;

[0081] It should be noted that the distance between the camera and the perfume refers to the straight-line distance from the object to the center of the camera, which is a key factor affecting the display size, clarity, and perspective effect of the perfume on the screen. When the perfume rotates, the perfume may face towards or away from the camera, so the distance between the camera and the perfume will change.

[0082] Determine the field of view angle of the camera, and mark the field of view angle of the camera as: FOV, and calculate the screen space error accumulation coefficient. The calculation formula is:

[0083] ; where is the screen space error accumulation coefficient.

[0084] It can be seen from the formula that the larger the screen space error accumulation coefficient, the greater the error in the rendering process of the user-defined perfume rendering scheme, indicating that the rendering effect is worse. Therefore, the performance of the user-defined perfume rendering scheme in a 3D scene is poor and may need to be appropriately optimized and adjusted to improve the rendering accuracy and effect.

[0085] The acquisition logic of the curvature change coefficient is as follows: Determine the special marking points of each style of the perfume component, construct a discrete grid of the perfume component, determine the LBO values of the special marking points before component combination in the user-defined perfume rendering scheme, and mark the LBO values of the special marking points before component combination in the user-defined perfume rendering scheme as: Mark the LBO values of the special marking points after component combination in the user-defined perfume rendering scheme as: where g = 1, 2, 3, ……, G, G is a positive integer, and g is the number of the special marking point;

[0086] It should be noted that in the 3D model of the perfume component, points with important geometric or visual features are selected as special marking points, such as connection points, boundary points, points with large curvature changes, etc. The special marking points are set by professionals in the field. The special marking points may deform and have alignment errors during different component combinations. Therefore, their LBO values can be used as important parameters to measure geometric changes.

[0087] Calculate the curvature change coefficient, and the calculation formula is: ; where is the curvature change coefficient, f(*) is the Laplace - Beltrami operator, is the L1 norm.

[0088] It can be seen from the formula that the larger the curvature change coefficient, the more significant the local curvature change caused by the splicing of components, the sudden change of the surface of the transition region between components, which may affect the rendering quality. Compared with the original components, the surface structure of the combined form changes significantly, resulting in an increase in the LBO difference.

[0089] The acquisition logic of the geometric variation metric coefficient is as follows: Determine the vertex set of the 3D models of adjacent perfume components after component combination in the user-defined perfume rendering scheme, mark the point sets of two adjacent components as A and B, and measure the maximum and minimum distances between the point sets of two adjacent components through the Hausdorff distance. The calculation formula is: ; is the Hausdorff distance between two adjacent components, is the minimum distance from the point set in set A to set B, is the minimum distance from the point set in set B to set A;

[0090] It should be noted that adjacent perfume components include the nozzle and the middle cover, the bottle body and the bottle cap, the nozzle and the bottle body, the nozzle and the bottle cap, the bottle body and the middle cover, the bottle cap and the middle cover, etc., which are specifically determined by the user-defined perfume rendering scheme.

[0091] Obtain the Hausdorff distance between different adjacent perfume components in the user-defined perfume rendering scheme, and calculate the geometric variation metric coefficient. The calculation formula is as follows: ; where w = 1, 2, 3, ……, L, L is a positive integer, and w is the number of different adjacent perfume components. is the geometric variation metric coefficient.

[0092] As can be seen from the formula, the larger the geometric variation metric coefficient, the lower the matching degree between components, the larger the assembly error, or the more serious the rendering deformation. The Hausdorff distance can be reduced by optimizing the component shape, improving the alignment accuracy, adjusting the rotation angle, and optimizing the rendering parameters, so as to improve the 3D assembly accuracy and rendering quality of the perfume bottle components.

[0093] Comprehensively analyze the rendering quality information and matching degree information of the perfume rendering scheme, and perform weighted calculations through the screen space error accumulation coefficient, the curvature change coefficient, and the geometric variation metric coefficient to construct a perfume rendering evaluation model and generate a perfume rendering evaluation coefficient. The calculation formula of the perfume rendering evaluation coefficient is as follows: ; where is the perfume rendering evaluation coefficient. , , are the proportionality coefficients of the screen space error accumulation coefficient, the curvature change coefficient, and the geometric variation metric coefficient respectively. , , are all greater than 0.

[0094] As can be seen from the formula, the larger the screen space error accumulation coefficient, the curvature change coefficient, and the geometric variation metric coefficient, the smaller the perfume rendering evaluation coefficient, indicating that the rendering effect of the user-defined perfume rendering scheme in the three-dimensional scene is poor. On the contrary, the smaller the screen space error accumulation coefficient, the curvature change coefficient, and the geometric variation metric coefficient, the larger the perfume rendering evaluation coefficient, indicating that the rendering effect of the user-defined perfume rendering scheme in the three-dimensional scene is good.

[0095] Set the perfume rendering evaluation coefficient threshold, and compare the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme with the perfume rendering evaluation coefficient threshold. If the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme is less than the perfume rendering evaluation coefficient threshold, a warning signal will be generated, indicating that the rendering effect of the user-defined perfume rendering scheme in the three-dimensional scene is poor. If the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme is greater than the perfume rendering evaluation coefficient threshold, no warning signal will be generated, indicating that the rendering effect of the user-defined perfume rendering scheme in the three-dimensional scene is good, indicating that the user-defined perfume rendering scheme can display the actual perfume situation.

[0096] Based on the user's historical data, knowledge graph, and 3D rendering analysis, the present invention optimizes the recommendation and rendering quality of the user-defined perfume rendering scheme. By using the user's historical data and knowledge graph, similar users are determined, the user's preferences for different perfume components are predicted, and the correlation between the perfume component styles is established. Before the user performs 3D rendering, the perfume component styles that meet the user's preferences are recommended, and the corresponding 3D models are pre-loaded to improve the rendering efficiency. Through discrete analysis, the geometric error of the perfume components at different rotation angles is evaluated, the longest folding edge is judged, the overall rendering quality is determined, and the matching degree between the overall components is determined, so as to comprehensively evaluate the performance of the user-defined perfume rendering scheme in the 3D scene. The present invention helps to improve the quality and interactive experience of user-defined perfume design, while reducing the rendering calculation burden and improving the system performance.

[0097] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0098] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more available medium sets. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0099] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0101] In several embodiments provided by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0102] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0103] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A selective 3D rendering method based on a three-dimensional scene, characterized in that: The specific steps include: S1: Determine similar users through historical data of users, determine users’ evaluation information on different perfume components, and determine users’ association information on different perfume component styles based on the positions of perfume component labels in the knowledge graph; S2: Before the rendering process of the user-defined perfume rendering scheme, the recommended styles of different components are determined, and the 3D model data of the recommended styles are loaded in the background; S3: according to the user-defined perfume rendering scheme, determining the rendering quality information of the perfume rendering scheme by discretizing and analyzing the longest folded edge of the perfume rendering scheme at different rotation angles; S4: Determine the curvature feature change of each special marking point on the grid through the special marking points before and after the component combination rendering in the perfume rendering scheme, and measure the geometric shape change through the Hausdorff distance to determine the matching information of the perfume rendering scheme; S5: comprehensively analyzing the rendering quality information and matching degree information of the perfume rendering scheme to determine the rendering performance of the user's perfume rendering scheme in the three-dimensional scene; Among them, the rendering quality information of the perfume rendering scheme is represented by the screen space error accumulation coefficient; The logic for obtaining the screen space error accumulation coefficient is: In the process of rendering the user-defined perfume rendering scheme, the longest folded edge of the perfume rendering scheme at different rotation angles is discretized and analyzed to determine the geometric error based on the longest folded edge. The geometric error of the longest folded edge at different rotation angles with the x-axis as the rotation axis is marked as: , the geometric error of the longest folded edge at different rotation angles with the y-axis as the rotation axis is marked as: , the geometric error of the longest folded edge at different rotation angles with the z-axis as the rotation axis is marked as: ,in, , J is a positive integer, , , They are the numbers of the rotation angles about the x-axis, y-axis, and z-axis respectively; Determine the height of the perfume on the screen in the custom perfume rendering scheme, and mark the height of the perfume on the screen at different rotation angles of the x-axis, y-axis, and z-axis as follows: , as well as ; Determine the distance between the perfume and the camera in the custom perfume rendering scheme, and mark the distance between the perfume and the camera at different rotation angles of the x-axis, y-axis, and z-axis as follows: , as well as ; Determine the camera's field of view and mark it as FOV. Calculate the screen space error accumulation coefficient using the following formula: ;in, is the screen space error accumulation coefficient.

2. The selective 3D rendering method based on a three-dimensional scene according to claim 1, characterized in that: Determine user evaluation information and relevance information for different fragrance components, including: Collect historical data of users' selection of perfume component styles, find other users similar to the user through collaborative filtering of historical data, and predict the target user's possible interest in unrated component styles through weighted average method based on similar users, determine the evaluation information of different perfume component styles, and represent the evaluation information of different perfume component styles through interest prediction value coefficients; Based on the historical data of users selecting perfume component styles, determine the labels of the perfume group styles selected by users. Based on the positions of the labels of the perfume group styles in the knowledge graph, determine the degree to which users may be interested in other component styles. Determine the correlation information of different perfume component styles, and represent the correlation information of different perfume component styles through the graph connection distance coefficient.

3. The selective 3D rendering method based on a three-dimensional scene according to claim 2, characterized in that: The acquisition logic of the predicted value coefficient of interest is: According to the historical data of the user's selection of the perfume component style, the score of the user's historical selection of the perfume component style is obtained, and the score of the user's historical selection of the perfume component style is marked as: , identify other users who selected the same fragrance component style as the user, and mark the scores of other users' selections of fragrance component style as: , where i=1, 2, 3, ..., I, where I is a positive integer, i is the number of the perfume component style selected by the user in history, and n=1, 2, 3, ..., N, where N is a positive integer, and n is the number of other users who selected the same perfume component style as the user; The cosine similarity is used to measure the similarity of user behaviors, and a similarity threshold is set to filter similar users. The calculation formula is: ;in, is the similarity between the nth other user and the user; Obtain users whose similarity with other users is greater than the similarity threshold, and record other users whose similarity with other users is greater than the similarity threshold as similar users. Calculate the user's interest prediction value coefficient for other component styles by weighted average. The calculation formula is: ;in, is the coefficient of the predicted value of interest for the kth component style.

4. The selective 3D rendering method based on a three-dimensional scene according to claim 3, characterized in that: The logic for obtaining the graph connection distance coefficient is: According to the historical data of the perfume component style selected by the user, the label of the perfume component style selected by the user is obtained, and the label of the perfume component style is used as the parent node in the knowledge graph. Different perfume component styles belong to the child nodes under the parent node. The nodes are connected by edges, and the length of the edge indicates the strength of the relationship; According to the ratings of the perfume component styles selected by the user in history, the total ratings of different parent nodes are determined. The calculation formula is: ,in, is the total score of the user in different parent nodes, is the score of the user's qth perfume component style under the mth parent node; Determine the graph connection distance coefficient of different perfume component styles, and the calculation formula is: ;in, is the graph connection distance coefficient of the kth perfume component style, is the relationship weight between the k-th perfume component style and the parent node, and JD is the total score of the parent node of the k-th perfume component style.

5. The selective 3D rendering method based on a three-dimensional scene according to claim 4, characterized in that: Identify recommended styles for different components, including: The evaluation information and correlation information of different perfume component styles are comprehensively analyzed, and the recommended evaluation coefficients of different perfume component styles are determined by weighted calculation of the interest prediction value coefficient and the graph connection distance coefficient. The calculation formula of the recommended evaluation coefficient is: ;in, is the recommended evaluation coefficient, , are the proportional coefficients of the prediction value coefficient of interest and the graph connection distance coefficient, respectively. , They are both greater than 0; Set the recommended evaluation coefficient threshold, compare the recommended evaluation coefficients of different perfume component styles with the recommended evaluation coefficient threshold. If the recommended evaluation coefficients of different perfume component styles are greater than the recommended evaluation coefficient threshold, the perfume component style will be loaded before the user customizes the perfume rendering scheme. If the recommended evaluation coefficients of different perfume component styles are less than the recommended evaluation coefficient threshold, no operation will be performed on the perfume component style.

6. The selective 3D rendering method based on a three-dimensional scene according to claim 5, characterized in that: Determine the rendering quality information and matching information of the perfume rendering scheme, including: Users form a custom perfume rendering scheme by selecting different perfume component styles. By comparing the rendering effects of the perfume components in the custom perfume rendering scheme before and after combination, the rendering quality information and matching information of the perfume rendering scheme are determined. The matching information of the perfume rendering scheme is represented by the curvature variation coefficient and the geometric variation measurement coefficient.

7. The selective 3D rendering method based on a three-dimensional scene according to claim 6, characterized in that: The acquisition logic of the curvature variation coefficient is: Determine the special marking points of each style of the perfume component, construct a discrete grid of the perfume component, determine the LBO value of the special marking point of the user-defined perfume rendering scheme before the component combination, and mark the LBO value of the special marking point of the user-defined perfume rendering scheme before the component combination as: , mark the LBO value of the special marking point of the user-defined perfume rendering scheme after the component combination as: , where g=1, 2, 3, ..., G, G is a positive integer, and g is the number of the special marking point; Calculate the curvature variation coefficient, the calculation formula is: ;in, is the curvature variation coefficient, f(*) is the Laplace-Beltrami operator, is the L1 norm.

8. The selective 3D rendering method based on a three-dimensional scene according to claim 7, characterized in that: The acquisition logic of the geometric variation coefficient is: Determine the vertex sets of the three-dimensional models of adjacent perfume components after the components are combined in the user-defined perfume rendering scheme, mark the point sets of the two adjacent components as A and B, and measure the maximum and minimum distances between the point sets of the two adjacent components by the Hausdorff distance. The calculation formula is: ; is the Hausdorff distance between two adjacent components, is the minimum distance from the points in set A to set B, is the minimum distance from the point set in set B to set A; Obtain the Hausdorff distances of different adjacent perfume components in the user-defined perfume rendering scheme and calculate the geometric variation coefficient. The calculation formula is: ; Wherein, w=1, 2, 3, ..., L, L is a positive integer, and w is the number of different adjacent perfume components, is the geometric coefficient of variation.

9. The selective 3D rendering method based on a three-dimensional scene according to claim 8, characterized in that: Determine how the user's perfume rendering solution will render in the 3D scene, including: The rendering quality information and matching degree information of the perfume rendering scheme are comprehensively analyzed. The screen space error accumulation coefficient, curvature change coefficient and geometric variation measurement coefficient are weighted to construct a perfume rendering evaluation model and generate a perfume rendering evaluation coefficient. The calculation formula of the perfume rendering evaluation coefficient is: ;in, is the perfume rendering evaluation coefficient, , , They are the proportional coefficients of screen space error accumulation coefficient, curvature variation coefficient, and geometric variation measurement coefficient, respectively. , , They are both greater than 0; Setting a perfume rendering evaluation coefficient threshold, comparing the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme with the perfume rendering evaluation coefficient threshold, and generating a warning signal if the perfume rendering evaluation coefficient of the user-defined perfume rendering scheme is less than the perfume rendering evaluation coefficient threshold; If the fragrance rendering evaluation coefficient of the user-defined fragrance rendering scheme is greater than the fragrance rendering evaluation coefficient threshold, no warning signal is generated.

Images