A system and method for quickly generating a panoramic dynamic image online based on a 3D model

Abstract

The present invention discloses a system and method for quickly generating a panoramic dynamic image online based on a 3D model, comprising the following steps: S1, obtaining 3D model data and generating a 3D model; S2, simplifying the 3D model and obtaining images from different perspectives using the 3D model; S3, synthesizing the images from different perspectives into a panoramic image through an improved gradient transition algorithm; S4, updating the lighting effect of the panoramic image according to the light intensity; S5, using the particle system technology to generate a dynamic effect for the panoramic image; S6, performing rendering processing through a cloud cluster and dynamically adjusting the image compression ratio using an improved adaptive compression technology; S7, adjusting the rendering angle and dynamic effect of the panoramic image in real time. The present invention can provide an efficient and scientific optimization scheme in 3D image generation, bringing significant technical value and economic benefits to practical applications.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D modeling and image rendering, and particularly to a system and method for online fast generation of panoramic dynamic images based on 3D models. Background Art

[0002] With the development of computer graphics and virtual reality technologies, generating panoramic dynamic images based on 3D models has become a key technology in many application fields, and is widely used in multiple industries such as games, movie production, virtual reality, and augmented reality. Panoramic dynamic images not only need to display the details of 3D models from multiple perspectives, but also require providing high-quality images and smooth user interaction experiences during the rendering process. To achieve these goals, existing technologies rely on a series of complex rendering and optimization algorithms, usually combining technologies such as 3D modeling, dynamic lighting, particle systems, and image compression.

[0003] Most of the existing panoramic image generation methods focus on how to generate static panoramic images or process relatively simple dynamic scenes. Traditional image generation methods usually use static 3D models to generate panoramic images and perform rendering through fixed rendering rules. This method often has significant bottlenecks when dealing with dynamic perspectives or complex image special effects. For example, existing image rendering technologies often struggle to balance rendering efficiency and image quality when facing a large number of perspective changes, resulting in an increase in the delay of the image generation process, especially in resource-constrained devices or network environments. In addition, although image compression technologies can reduce the amount of transmitted data to a certain extent, they often affect image quality. Especially when the network bandwidth is limited or the image detail requirements are high, the efficiency and adaptability of compression algorithms are still insufficient.

[0004] In existing panoramic dynamic image generation methods, although some technologies have been able to achieve dynamic perspective adjustment and real-time image rendering, these methods still face great challenges in balancing the delay, image quality, and transmission efficiency during user interaction. For example, many existing systems cannot adapt to different network conditions and device performances, resulting in limited image quality. Especially during dynamic interaction, the details and smoothness of images often cannot meet the user's experience requirements. In addition, traditional rendering and compression technologies often cannot adaptively adjust the compression ratio or image rendering strategy when the network bandwidth is low or the device performance is weak, resulting in problems such as delay and frame stuttering for users during use.

[0005] At present, image generation methods based on cloud distributed rendering architectures have also begun to be gradually applied in this field. Cloud rendering realizes efficient parallel computing by distributing computing tasks to multiple computing nodes, which can effectively improve the rendering speed. However, existing cloud rendering technologies often have data transmission bottlenecks when dealing with high-complexity images. The real-time transmission and rendering of images require a large amount of bandwidth and computing resources, which is still a huge challenge for environments with poor device performance or unstable networks. Especially for the generation of large-scale panoramic dynamic images, how to ensure the rendering quality while avoiding image distortion or delay caused by bandwidth limitations is still an urgent problem to be solved.

[0006] In view of the above deficiencies of the existing technologies, the present invention proposes a system and method for online rapid generation of panoramic dynamic images based on 3D models. By introducing dynamic lighting calculation, particle systems, distributed rendering architectures, and adaptive image compression technologies, this method aims to achieve high-quality generation and efficient transmission of panoramic dynamic images. Compared with traditional technologies, the present invention not only optimizes the perspective dynamic adjustment and image detail enhancement during rendering, but also can automatically adjust the image compression ratio and rendering strategy according to device performance and network conditions, effectively solving problems such as image generation delay, low transmission efficiency, and quality loss existing in the prior art. In addition, based on the cloud distributed rendering architecture, the present invention can achieve efficient parallel computing through dynamic scheduling and load balancing in resource-constrained environments, ensuring that users obtain a smooth and delay-free panoramic dynamic image experience during the interaction process.

[0007] Therefore, the present invention not only solves the performance bottleneck and user experience problems faced by the existing technologies, but also provides a more flexible and efficient solution for future panoramic dynamic image generation and rendering technologies. Summary of the Invention

[0008] An object of the present invention is to propose a system and method for online rapid generation of panoramic dynamic images based on 3D models. The present invention can provide an efficient and scientific optimization solution in the generation of panoramic dynamic images, bringing significant technical value and economic benefits to practical applications.

[0009] According to a method for online rapid generation of panoramic dynamic images based on 3D models of the present invention, the following steps are included:

[0010] S1. Obtain 3D model data and generate a 3D model through a 3D modeling tool;

[0011] S2. Simplify the 3D model and obtain images from different perspectives by using the 3D model;

[0012] S3. Synthesize panoramic images from images of different perspectives through an improved gradient transition algorithm, and the improved gradient transition algorithm performs smooth transitions of colors and textures on the stitching area;

[0013] S4. Perform real-time calculations on lighting, shadows, and reflections in the panoramic image, and update the lighting effect of the panoramic image according to the light intensity;

[0014] S5. Use particle system technology to generate dynamic effects for the panoramic image;

[0015] S6. Utilize a cloud distributed rendering architecture to allocate image generation tasks to multiple computing nodes, perform rendering processing through a cloud cluster, and adopt an improved adaptive compression technology to dynamically adjust the image compression ratio in real time according to the user's network conditions and device performance;

[0016] S7. According to the user's operation instructions, adjust the rendering angle and dynamic effects of the panoramic image in real time.

[0017] Optionally, the S2 specifically includes:

[0018] S21. Use the QEM algorithm to simplify the 3D model. The QEM algorithm calculates the geometric error of each triangular patch, and the geometric error is represented by an error matrix Q. The error matrix Q is the distance error between the vertex coordinates of each triangular patch and the geometric center point of the adjacent patch of the vertex coordinates:

[0019] ;

[0020] Among them, is the coordinate of each vertex on the triangular patch, is the geometric center coordinate of the triangular patch, and n is the number of vertices;

[0021] Apply the error matrix Q of each triangular patch to the simplification process, and preferentially select the triangular patch with the smallest error for simplification according to the error size. The geometric position of the new triangular patch after simplification is the weighted average of its error matrix Q;

[0022] Iteratively update the new triangular patch after simplification, continue to select the triangular patch with the smallest error for merging until the preset number M of triangular patches is reached;

[0023] S22. Perform streamlining processing on the texture mapping of the 3D model, and obtain the texture block size T based on the number M of triangular patches;

[0024] S23. Evaluate the error between the simplified model and the original model through an error metric standard to obtain an error threshold . If the error is less than the preset threshold, it is determined that the simplified model is qualified.

[0025] Optionally, S3 specifically includes:

[0026] S31. Smoothly transition multiple perspective images using an improved gradual transition algorithm, and the improved gradual transition algorithm performs smoothing using a gradual transition factor G(x, y):

[0027] ;

[0028] where d(x, y) represents the distance between the pixel point (x, y) in the stitching area and the stitching edge, is the maximum distance, C(x, y) represents the color value of the pixel point (x, y), is the average value of the color in this area, , is an adjustment factor, is the natural exponential function;

[0029] S32. Obtain the smoothed perspective image according to the gradual transition factor G(x, y):

[0030] ;

[0031] where, is the smoothed pixel value, is the pixel value of the previous image before stitching, is the pixel value of the next image after stitching;

[0032] S33. Synthesize multiple perspective images into a panoramic image.

[0033] Optionally, S4 specifically includes:

[0034] S41. Calculate the light intensity I(x, y) in the panoramic image in real time:

[0035] ;

[0036] where, is the ambient light, is the diffuse reflection light, is the specular reflection light, is the refraction light, is the scattered light;

[0037] The diffuse reflection light is calculated as follows:

[0038] ;

[0039] where, is the influence coefficient of the incident angle of light on diffuse reflection, is the dot product of the surface normal and the light source direction, is the intensity of the light source;

[0040] The specularly reflected light is calculated as follows:

[0041] ;

[0042] where is the specular reflection coefficient, H is the half - vector, is the roughness exponent, V is the observer - direction vector, representing the unit vector pointing from the observer to the surface being rendered, is the dot - product of the surface normal and the light - source direction, is the dot - product of the surface normal and the half - vector, is the intensity of the light source;

[0043] The refracted light The calculation formula is:

[0044] ;

[0045] where is the Fresnel - Schlick approximation coefficient, is the refractive index of the medium, is the dot - product of the surface normal and the light - source direction, is the intensity of the light source;

[0046] The scattered light The calculation formula is:

[0047] ;

[0048] where and are the Rayleigh and Mie scattering coefficients respectively, is the dot - product of the surface normal and the light - source direction, is the adjustment coefficient, is the intensity of the light source;

[0049] S42. Update the lighting effect in the panoramic image according to the real - time calculated lighting intensity I(x, y).

[0050] Optionally, the S5 specifically includes:

[0051] S51. Apply the particle - system technology to generate dynamic effects in the panoramic image. The particle system consists of several particles, and the state of each particle is defined by the position P(x, y, z), velocity V(x, y, z), acceleration A(x, y, z) and the life - cycle When generating particles, the random - distribution and gradient - weight method is adopted, and the initial positions of the particles Randomize according to the spatial distribution within the region. When updating the position of the particles, model the movement of the particles using a Gaussian distribution;

[0052] S52. The update formula for the particle system is:

[0053] ;

[0054] ;

[0055] where, is the new position of the particle, is the initial position of the particle, is the new velocity of the particle, is the initial velocity of the particle, is the time step;

[0056] S53. According to the generated dynamic particle effect, adjust the movement trajectory of the particles in real time, and superimpose the generated dynamic effect onto the panoramic image.

[0057] Optionally, the specific steps of S6 include:

[0058] S61. Adopt a cloud distributed rendering architecture, allocate the image rendering tasks to multiple computing nodes, and the rendering nodes dynamically allocate tasks according to the load balancing algorithm:

[0059] ;

[0060] where, is the computing task volume of the i-th rendering node, is the load of the i-th rendering node, is the computing power of the i-th rendering node, is the bandwidth, N is the total number of nodes, is the load of the i-th node, is the computing power of the i-th node, is the bandwidth of the i-th node;

[0061] S62. Obtain the rendering priority of each image block:

[0062] ;

[0063] where, is the priority of the image block, is the rendering time of the i-th image, is the spatial complexity of the i-th image, , are adjustment factors;

[0064] S63. Adopt a rendering task transmission strategy based on multi-level network bandwidth control to dynamically adjust the bandwidth of data transmission:

[0065] ;

[0066] Among them, is the bandwidth allocation for the transmission task, is the maximum bandwidth, is the priority of the transmission task, and M is the number of tasks;

[0067] S64. Perform real-time feedback and adjustment on the calculation results of each rendering node, calculate the workload of the rendering node through the feedback mechanism and adjust the task allocation between nodes. The feedback adjustment formula is:

[0068] ;

[0069] Among them, is the adjusted rendering task volume of the i-th rendering node, is the calculation task volume of the i-th rendering node, is the global average load, is the load of the i-th rendering node, is the adjustment factor;

[0070] S65. Adopt an improved adaptive compression technology to dynamically adjust the image compression ratio in real time according to the user's network conditions and device performance to adapt to different bandwidths and device capabilities:

[0071] ;

[0072] Among them, is the compression ratio, is the transmission task bandwidth, is the priority of the image block, is the rendering task volume, is the node load, is the number of image blocks, is the image complexity, , are the adjustment coefficients, is the adaptive adjustment function, is the natural exponential function;

[0073] S66. Compress the panoramic image according to the compression ratio ;

[0074] S67. Combine the quality evaluation of the compressed image to optimize the adaptive compression algorithm. The quality of the compressed image is:

[0075] ;

[0076] Among them, is the entropy value of the image, is the data transmission delay, is the maximum allowable delay, is the delay impact coefficient, is the complexity impact coefficient, is the image complexity.

[0077] A system for online rapid generation of panoramic dynamic images based on 3D models according to an embodiment of the present invention includes the following modules:

[0078] The 3D model acquisition and processing module is used to collect 3D model data and perform simplification and optimization processing;

[0079] The panoramic image synthesis module is used to synthesize panoramic images from multiple perspective images through cube projection and calculate the overlapping parts of the stitching areas using image stitching technology;

[0080] The dynamic lighting and effect generation module is used to calculate lighting, shadows, reflections and dynamic effects in the panoramic image to generate natural dynamic effects;

[0081] The rendering and distributed computing module distributes rendering tasks to multiple computing nodes through a cloud distributed rendering architecture, dynamically optimizes task allocation according to node load, bandwidth and computing power, and performs parallel processing and optimization on the rendering results;

[0082] The adaptive compression and transmission optimization module is used to adjust the image compression ratio according to the user's network bandwidth and device performance, balance image quality and transmission efficiency using adaptive compression technology, and reduce transmission delay through network optimization strategies;

[0083] The real-time feedback and optimization module is used to feedback information according to the real-time rendering results and adjust image details and resolution.

[0084] The beneficial effects of the present invention are:

[0085] First of all, by innovatively introducing a variety of technologies, the present invention solves the performance bottleneck and user experience problems faced in the generation and rendering of panoramic dynamic images in the prior art. In terms of image generation, dynamic lighting calculation based on 3D models and particle systems are adopted, which makes the performance of panoramic dynamic images in terms of lighting, shadows, reflections and dynamic effects more realistic and delicate, improving the visual effect of the images. Compared with traditional static images or simple dynamic images, the introduction of dynamic lighting enables the images to adaptively adjust according to the light source and scene changes at different perspectives, enhancing the user's immersion.

[0086] Secondly, the present invention optimizes the calculation process of image rendering through a cloud distributed rendering architecture, distributes rendering tasks to multiple computing nodes, improves rendering efficiency, and the system can dynamically adjust the rendering load according to the network environment and device performance, avoiding rendering delays caused by insufficient computing power of a single node, ensuring the efficiency of large-scale panoramic dynamic image generation. This innovation effectively reduces the delays caused by computing bottlenecks in traditional rendering methods, and is especially suitable for devices and network environments with limited resources.

[0087] In addition, the present invention introduces an adaptive compression technology, which can automatically adjust the compression ratio and rendering details of the image according to the real-time network bandwidth and device performance. This enables the image to maintain high quality even in an environment of low bandwidth or low-performance devices, and reduces transmission delays. The adaptive compression algorithm can optimize the transmission efficiency while ensuring the image quality, avoiding quality loss and lag phenomena in traditional compression methods.

[0088] Finally, the perspective dynamic adjustment technology and real-time feedback optimization module of the present invention ensure efficient response during user interaction. When the user changes the perspective or operates, the system can calculate the new perspective in real time and optimize image rendering to ensure that the image quality and smoothness are not affected. Through these optimization measures, the present invention can provide a more smooth and natural user experience when processing complex dynamic images. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0090] Figure 1 is the overall flowchart of a method for online rapid generation of panoramic dynamic images based on a 3D model proposed by the present invention;

[0091] Figure 2 is the schematic structural diagram of a system for online rapid generation of panoramic dynamic images based on a 3D model proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0092] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0093] Refer to Figure 1 , a method for online rapid generation of panoramic dynamic images based on a 3D model, comprising the following steps:

[0094] S1. Obtain 3D model data and generate a 3D model through a 3D modeling tool;

[0095] S2. Simplify the 3D model and obtain images from different perspectives using the 3D model;

[0096] S3. Synthesize the images from different perspectives into a panoramic image through an improved gradient transition algorithm, and the improved gradient transition algorithm performs smooth transitions of colors and textures on the stitching areas;

[0097] S4. Perform real-time calculations on the lighting, shadows, and reflections in the panoramic image, and update the lighting effect of the panoramic image according to the light intensity;

[0098] S5. Use the particle system technology to generate dynamic effects for the panoramic image;

[0099] S6. Utilize the cloud distributed rendering architecture to allocate the image generation tasks to multiple computing nodes, perform rendering processing through the cloud cluster, and adopt an improved adaptive compression technology to dynamically adjust the image compression ratio in real time according to the user's network conditions and device performance;

[0100] S7. According to the user's operation instructions, adjust the rendering angle and dynamic effects of the panoramic image in real time.

[0101] In this embodiment, the S2 specifically includes:

[0102] S21. Use the QEM algorithm to simplify the mesh of the 3D model. The QEM algorithm calculates the geometric error of each triangular patch, and the geometric error is represented by the error matrix Q. The error matrix Q is the distance error between the vertex coordinates of each triangular patch and the geometric center point of the adjacent patch of the vertex coordinates:

[0103] ;

[0104] Among them, is the coordinate of each vertex on the triangular patch, is the geometric center coordinate of the triangular patch, and n is the number of vertices;

[0105] Apply the error matrix Q of each triangular patch to the simplification process, and preferentially select the triangular patch with the smallest error for simplification according to the error size. The geometric position of the new triangular patch after simplification is the weighted average of its error matrix Q;

[0106] Iteratively update the new triangular patch after simplification, and continue to select the triangular patch with the smallest error for merging until the preset number M of triangular patches is reached;

[0107] S22. Perform streamlining processing on the texture mapping of the 3D model, and obtain the texture block size T based on the number M of triangular patches;

[0108] S23. Evaluate the error between the simplified model and the original model using an error metric standard to obtain an error threshold. If the error is less than the preset threshold, it is determined that the simplified model is qualified.

[0109] In this embodiment, step S3 specifically includes:

[0110] S31. Use an improved gradual transition algorithm to smoothly transition multiple perspective images. The improved gradual transition algorithm uses a gradual factor G(x, y) for smoothing:

[0111] ;

[0112] where d(x, y) represents the distance between the pixel point (x, y) in the stitching area and the stitching edge, is the maximum distance, C(x, y) represents the color value of the pixel point (x, y), is the average color of this area, , is an adjustment factor, is the natural exponential function;

[0113] S32. Obtain the smoothly transitioned perspective images according to the gradual factor G(x, y):

[0114] ;

[0115] where, is the smoothly transitioned pixel value, is the pixel value of the previous image before stitching, is the pixel value of the next image after stitching;

[0116] S33. Synthesize multiple perspective images into a panoramic image.

[0117] In this embodiment, step S4 specifically includes:

[0118] S41. Calculate the light intensity I(x, y) in the panoramic image in real time:

[0119] ;

[0120] where, is the ambient light, is the diffuse reflection light, is the specular reflection light, is the refraction light, is the scattered light;

[0121] The diffuse reflection light is calculated as follows:

[0122] ;

[0123] Among them, is the influence coefficient of the incident angle of light on diffuse reflection, is the dot product of the surface normal and the light source direction, is the intensity of the light source;

[0124] The specularly reflected light is calculated as follows:

[0125] ;

[0126] Among them, is the specular reflection coefficient, H is the half-way vector, is the roughness exponent, V is the observer direction vector, representing the unit vector pointing from the observer to the surface being rendered, is the dot product of the surface normal and the light source direction, is the dot product of the surface normal and the half-way vector, is the intensity of the light source;

[0127] The refracted light The calculation formula is:

[0128] ;

[0129] Among them, is the Fresnel - Schlick approximation coefficient, is the refractive index of the medium, is the dot product of the surface normal and the light source direction, is the intensity of the light source;

[0130] The scattered light The calculation formula is:

[0131] ;

[0132] Among them, and are the Rayleigh and Mie scattering coefficients respectively, is the dot product of the surface normal and the light source direction, is the adjustment coefficient, is the intensity of the light source;

[0133] S42. Update the lighting effect in the panoramic image according to the real - time calculated lighting intensity I(x, y).

[0134] In this embodiment, the S5 specifically includes:

[0135] S51. Apply particle system technology to generate dynamic effects in panoramic images. The particle system consists of a number of particles, and the state of each particle is defined by position P(x, y, z), velocity V(x, y, z), acceleration A(x, y, z), and lifespan. When generating particles, the random distribution and gradient weight method are adopted, and the initial positions of the particles are randomized according to the spatial distribution within the region. When updating the positions of the particles, the Gaussian distribution is used to model the movement of the particles. When generating particles, the random distribution and gradient weight method are adopted, and the initial positions of the particles are randomized according to the spatial distribution within the region. When updating the positions of the particles, the Gaussian distribution is used to model the movement of the particles.

[0136] S52. The update formula of the particle system is:

[0137] ;

[0138] ;

[0139] where is the new position of the particle, is the initial position of the particle, is the new velocity of the particle, is the initial velocity of the particle, is the time step;

[0140] S53. According to the generated dynamic particle effects, the movement trajectories of the particles are adjusted in real time, and the generated dynamic effects are superimposed on the panoramic images.

[0141] In this embodiment, the specific content of S6 includes:

[0142] S61. Adopt a cloud distributed rendering architecture to allocate image rendering tasks to multiple computing nodes. The rendering nodes dynamically allocate tasks according to the load balancing algorithm:

[0143] ;

[0144] where is the computing task volume of the i-th rendering node, is the load of the i-th rendering node, is the computing power of the i-th rendering node, is the bandwidth, N is the total number of nodes, is the load of the i-th node, is the computing power of the i-th node, is the bandwidth of the i-th node;

[0145] S62. Obtain the rendering priority of each image block:

[0146] ;

[0147] where is the priority of the image block, is the rendering time of the i-th image block, is the spatial complexity of the i-th image block, 、 is the adjustment factor;

[0148] S63. Adopt a rendering task transmission strategy based on multi-level network bandwidth control to dynamically adjust the bandwidth of data transmission:

[0149] ;

[0150] Among them, is the bandwidth allocation of the transmission task, is the maximum bandwidth, is the priority of the transmission task, and M is the number of tasks;

[0151] S64. Perform real-time feedback and adjustment on the calculation results of each rendering node, calculate the workload of the rendering node through the feedback mechanism and adjust the task allocation between nodes. The feedback adjustment formula is:

[0152] ;

[0153] Among them, is the adjusted rendering task volume of the i-th rendering node, is the calculation task volume of the i-th rendering node, is the global average load, is the load of the i-th rendering node, is the adjustment factor;

[0154] S65. Adopt an improved adaptive compression technology to dynamically adjust the image compression ratio in real time according to the user's network conditions and device performance to adapt to different bandwidths and device capabilities:

[0155] ;

[0156] Among them, is the compression ratio, is the transmission task bandwidth, is the priority of the image block, is the rendering task volume, is the node load, is the number of image blocks, is the image complexity, 、 are the adjustment coefficients, is the adaptive adjustment function, is the natural exponential function;

[0157] S66. According to the compression ratio Compress the panoramic image;

[0158] S67. Optimize the adaptive compression algorithm in combination with the quality assessment of the compressed image, and the quality of the compressed image is:

[0159] ;

[0160] wherein, is the entropy value of the image, is the data transmission delay, is the maximum allowable delay, is the delay impact coefficient, is the complexity impact coefficient, is the image complexity.

[0161] Refer to Figure 2 , A system for online and fast generation of panoramic dynamic images based on a 3D model, including the following modules:

[0162] A 3D model acquisition and processing module for collecting 3D model data and performing simplification and optimization processing;

[0163] A panoramic image synthesis module for synthesizing panoramic images from multiple perspective images through cube projection and calculating the overlapping parts of the stitching areas using image stitching technology;

[0164] A dynamic lighting and effect generation module for calculating lighting, shadows, reflections, and dynamic effects in panoramic images to generate natural dynamic effects;

[0165] A rendering and distributed computing module that distributes rendering tasks to multiple computing nodes through a cloud distributed rendering architecture, dynamically optimizes task allocation according to node load, bandwidth, and computing power, and performs parallel processing and optimization on the rendering results;

[0166] An adaptive compression and transmission optimization module for adjusting the image compression ratio according to the user's network bandwidth and device performance, using adaptive compression technology to balance image quality and transmission efficiency, and reducing transmission delay through network optimization strategies;

[0167] A real-time feedback and optimization module for feeding back information based on real-time rendering results and adjusting image details and resolution.

[0168] Example 1:

[0169] Embodiment In an online education platform, with the rapid development of online education, virtual laboratories, as an innovative teaching tool, have gradually become an important part of many education platforms. Through virtual laboratories, students can conduct various experimental operations without actual equipment, master practical operation skills and engage in interactive learning. With the progress of technology, the requirements for the interactivity, realism, and diverse experimental scenarios of virtual laboratories are also increasing. Traditional virtual laboratory technologies often face problems such as image latency, slow loading, and low image quality, which affect students' immersion and learning efficiency.

[0170] Suppose an online education platform offers a physics course, and the course content includes various classical mechanics experiments, such as inclined plane slider experiments, spring oscillator experiments, etc. These experiments require precise physical modeling and interactive control. Students can simulate and operate these experimental devices in a virtual environment through the virtual laboratory. In the virtual laboratory, students can adjust experimental parameters, change experimental conditions, and view experimental results by clicking the mouse, operating the keyboard, or using virtual reality devices.

[0171] In the application of this virtual laboratory, all experimental devices (such as inclined planes, sliders, springs, etc.) are constructed in the form of 3D models and are rendered in the background through the system of the present invention for online rapid generation of panoramic dynamic images based on 3D models. When students conduct experiments, each operation will immediately affect the experimental results and be reflected in the virtual laboratory.

[0172] Through the cloud distributed rendering architecture of the present invention, all rendering tasks are assigned to multiple computing nodes, and the load balancing algorithm is used to optimize the rendering speed, reduce image latency, and ensure that each student's operation can be quickly reflected in the experimental scenario. At the same time, in order to adapt to different network conditions and the performance of students' devices, the system adopts adaptive compression technology to reduce the latency and bandwidth pressure of data transmission while ensuring the quality of experimental images, ensuring smooth loading and real-time update of images. During the experiment, students can adjust the parameters of experimental devices through simple interactions (such as mouse dragging, button clicking). Whenever the viewing angle changes or the experimental parameters change, the system will quickly update the image according to the students' operations through the viewing angle dynamic adjustment module and the rendering module to ensure instant feedback of operations.

[0173] To verify the beneficial effects of the present invention, the implementer conducted experimental tests in a real online education platform. The experimental scenario was a student conducting an inclined plane slider experiment in the virtual laboratory. In this experiment, the student could drag the slider with the mouse and change the angle of the inclined plane to observe the movement of the slider. The implementer tested the image rendering speed, loading latency, and image quality of this experiment under different network bandwidth conditions and device configurations.

[0174] By comparing with the traditional method, the specific data is shown in Table 1 below:

[0175] Table 1 Comparison of Image Rendering Speed and Loading Delay Results between Traditional Method and the Method of the Present Invention

[0176] ;

[0177] In the whole embodiment, the implementer not only solves the problems of image rendering delay, slow loading and low image quality in the virtual laboratory by the method of the present invention, but also improves the interaction efficiency of the laboratory system, realizing the efficient operation of virtual experiments and a smooth user experience.

[0178] The present invention optimizes the image rendering and loading process in the virtual laboratory by introducing dynamic lighting calculation based on 3D models, cloud distributed rendering architecture and adaptive compression technology. In the traditional technology, the delay of image rendering and the lag phenomenon in low-bandwidth environments seriously affect the interaction experience of students. However, the present invention significantly improves the rendering speed and image quality and reduces the loading delay by dynamically adjusting the image compression ratio and real-time optimizing the rendering strategy, thus enhancing the overall performance of the virtual laboratory.

[0179] The adaptive compression technology introduced by the present invention dynamically adjusts the compression ratio of images according to device performance and network bandwidth. While ensuring image quality, it reduces the delay of data transmission and bandwidth pressure. Compared with traditional static compression algorithms, the adaptive compression technology of the present invention can not only improve the loading efficiency in low-bandwidth environments, but also adjust the compression strategy according to the complexity and real-time changes of experimental content to ensure that each student can obtain a smooth virtual experiment experience in different network environments.

[0180] The present invention optimizes the distributed rendering architecture, reasonably distributes computing tasks to multiple computing nodes, reduces the delay caused by insufficient computing resources during the rendering process. In high-load situations, the system can intelligently perform load balancing to ensure the speed and quality of image rendering. Through a real-time feedback mechanism, the system can flexibly adjust the rendering strategy during the student interaction process to ensure that every operation in the experimental scene can be promptly responded to.

[0181] The above is only the preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for quickly generating a panoramic dynamic image online based on a 3D model, characterized in that, It includes the following steps: S1. Obtain 3D model data and generate a 3D model through a 3D modeling tool; S2. Simplify the 3D model and obtain images from different perspectives using the 3D model; S3. Synthesize the images from different perspectives into a panoramic image through an improved gradient transition algorithm, and the improved gradient transition algorithm performs smooth transitions of colors and textures on the stitching areas; S4. Perform real-time calculations on the lighting, shadows, and reflections in the panoramic image, and update the lighting effect of the panoramic image according to the light intensity; S5. Use the particle system technology to generate dynamic effects for the panoramic image; S6. Utilize a cloud distributed rendering architecture to distribute the image generation tasks to multiple computing nodes, perform rendering processing through a cloud cluster, and adopt an improved adaptive compression technology to dynamically adjust the image compression ratio in real time according to the user's network conditions and device performance; S7. According to the user's operation instructions, adjust the rendering angle and dynamic effects of the panoramic image in real time; The specific content of S3 includes: S31. Use an improved gradient transition algorithm to perform smooth transitions on multiple perspective images, and the improved gradient transition algorithm uses a gradient factor G(x,y) for smoothing: ； Among them, d(x,y) represents the distance between the pixel point (x,y) in the splicing area and the splicing edge, is the maximum distance, C(x,y) represents the color value of the pixel point (x,y), is the average value of the color of this area, 、 is the adjustment factor, is the natural exponential function; S32. Obtain the smoothed perspective images according to the gradient factor G(x,y); ； Among them, is the smoothed pixel value, is the pixel value of the previous image to be stitched, is the pixel value of the next image to be stitched; S33. Synthesize multiple perspective images into a panoramic image.

2. The method for online and quickly generating a panoramic dynamic image based on a 3D model according to claim 1, wherein The specific content of S2 includes: S21. Use the QEM algorithm to perform mesh simplification on the 3D model. The QEM algorithm calculates the geometric error of each triangular patch, and the geometric error is represented by an error matrix Q. The error matrix Q is the distance error between the vertex coordinates of each triangular patch and the geometric center point of the adjacent patch of the vertex coordinates: ； Among them, is the coordinate of each vertex on the triangular patch, is the coordinate of the geometric center of the triangular patch, and n is the number of vertices; Apply the error matrix Q of each triangular patch to the simplification process, preferentially select the triangular patch with the smallest error according to the error size for simplification, and the geometric position of the new triangular patch after simplification is the weighted average of its error matrix Q; Perform iterative updates on the new triangular patches after simplification, continue to select the triangular patches with the smallest error for merging until the preset number M of triangular patches is reached; S22. Perform streamlining processing on the texture mapping of the 3D model, and obtain the texture block size T based on the number M of triangular patches; S23. Perform error evaluation on the simplified model and the original model through an error metric standard to obtain an error threshold . If the error is less than the preset threshold, it is determined that the simplified model is qualified.

3. A method for online and quickly generating a panoramic dynamic image based on a 3D model according to claim 1, characterized in that, The specific content of S4 includes: S41. Perform real-time calculations on the light intensity I(x,y) in the panoramic image; ； Among them, is ambient light, is diffuse reflection light, is specular reflection light, is refracted light, is scattered light; The diffuse reflection light is calculated as follows: ； Among them, is the influence coefficient of the incident angle of light on diffuse reflection, is the dot product of the surface normal and the light source direction, is the intensity of the light source; The specularly reflected light is calculated as follows: ； Among them, is the specular reflection coefficient, H is the half-way vector, is the roughness exponent, V is the observer direction vector, representing the unit vector pointing from the observer to the surface being rendered, is the dot product of the surface normal and the light source direction, is the dot product of the surface normal and the half-way vector, is the intensity of the light source; The refracted light The calculation formula is as follows: ； Among them, is the Fresnel-Schlick approximation coefficient, is the refractive index of the medium, is the dot product of the surface normal and the light source direction, is the intensity of the light source; The scattered light The calculation formula is as follows: ； Among them, and are the Rayleigh and Mie scattering coefficients respectively, is the dot product of the surface normal and the light source direction, is the adjustment coefficient, is the intensity of the light source; S42. Update the lighting effect in the panoramic image according to the real-time calculated light intensity I(x,y).

4. A method for online and quickly generating a panoramic dynamic image based on a 3D model according to claim 1, characterized in that The specific content of S5 includes: S51. Apply particle system technology to generate dynamic effects in panoramic images. The particle system consists of several particles, and the state of each particle is defined by position P(x, y, z), velocity V(x, y, z), acceleration A(x, y, z), and life cycle. When generating particles, the random distribution and gradient weight method are adopted, and the initial positions of the particles are randomized according to the spatial distribution within the region. When updating the positions of the particles, the Gaussian distribution is used to model the movement of the particles. Defined, when generating particles, the random distribution and gradient weight method are adopted, and the initial positions of the particles Are randomized according to the spatial distribution within the region. When updating the positions of the particles, the Gaussian distribution is used to model the movement of the particles; S52. The update formula of the particle system is: ； ； Among them, is the new position of the particle, is the initial position of the particle, is the new velocity of the particle, is the initial velocity of the particle, is the time step; S53. According to the generated dynamic particle effects, adjust the movement trajectories of the particles in real time, and superimpose the generated dynamic effects on the panoramic image.

5. A method for online and quickly generating a panoramic dynamic image based on a 3D model according to claim 1, wherein The specific content of S6 includes: S61. Adopt a cloud distributed rendering architecture to distribute the image rendering tasks to multiple computing nodes, and the rendering nodes dynamically allocate tasks according to the load balancing algorithm; ； Among them, is the computing task volume of the i-th rendering node, is the load of the i-th rendering node, is the computing power of the i-th rendering node, is the bandwidth, N is the total number of nodes, is the load of the i-th node, the computing power of the i-th node, is the bandwidth of the i-th node; S62. Obtain the rendering priority of each image block; ； Among them, is the priority of the image block, is the rendering time of the i-th image block, is the spatial complexity of the i-th image block, , are adjustment factors; S63. Adopt a rendering task transmission strategy based on multi-level network bandwidth control to dynamically adjust the bandwidth of data transmission; ； wherein, is the bandwidth allocation for the transmission task, is the maximum bandwidth, is the priority of the transmission task, and M is the number of tasks; S64. Provide real-time feedback and adjustment for the calculation results of each rendering node. Calculate the workload of the rendering nodes through the feedback mechanism and adjust the task allocation between the nodes. The feedback adjustment formula is as follows: ； wherein, is the adjusted rendering task volume of the i-th rendering node, is the computing task volume of the i-th rendering node, is the global average load, is the load of the i-th rendering node, is the adjustment factor; S65. Adopt an improved adaptive compression technology to dynamically adjust the image compression ratio in real time according to the user's network conditions and device performance to adapt to different bandwidths and device capabilities: ； Among them, is the compression ratio, is the transmission task bandwidth, is the priority of the image block, is the rendering task volume, is the node load, is the number of image blocks, is the image complexity, 、 is the adjustment coefficient, is the adaptive adjustment function, is the natural exponential function; S66. Compress the panoramic image according to the compression ratio Perform compression on the panoramic image; S67. Optimize the adaptive compression algorithm in combination with the quality assessment of the compressed image, and the quality of the compressed image is as follows: ； wherein, is the entropy value of the image, is the data transmission delay, is the maximum allowable delay, is the delay impact coefficient, is the complexity impact coefficient, is the image complexity.

6. A system for online and rapid generation of panoramic dynamic images based on a 3D model, which executes the method for online and rapid generation of panoramic dynamic images based on a 3D model according to any one of claims 1 to 5, characterized in that, It includes the following modules: 3D model acquisition and processing module, which is used to collect 3D model data and perform simplification and optimization processing; Panoramic image synthesis module, which is used to synthesize panoramic images from multiple perspective images through cube projection and calculate the overlapping parts of the stitching areas using image stitching technology; Dynamic lighting and effect generation module, which is used to calculate the lighting, shadows, reflections, and dynamic effects in the panoramic images to generate natural dynamic effects; Rendering and distributed computing module, which distributes rendering tasks to multiple computing nodes through a cloud distributed rendering architecture, dynamically optimizes task allocation according to node load, bandwidth, and computing power, and performs parallel processing and optimization on the rendering results; Adaptive compression and transmission optimization module, which is used to adjust the image compression ratio according to the user's network bandwidth and device performance, adopts adaptive compression technology to balance image quality and transmission efficiency, and reduces transmission latency through network optimization strategies at the same time; Real-time feedback and optimization module, which is used to feedback information based on the real-time rendering results and adjust the image details and resolution.

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