CN117635791B

CN117635791B - 3D model presentation method and system, storage medium and electronic equipment

Info

Publication number: CN117635791B
Application number: CN202311531353.XA
Authority: CN
Inventors: 何东林; 张立; 牛国栋
Original assignee: Chongqing Xianghe Daewoo Packaging Co ltd
Current assignee: Chongqing Xianghe Daewoo Packaging Co ltd
Priority date: 2023-11-16
Filing date: 2023-11-16
Publication date: 2024-05-31
Anticipated expiration: 2043-11-16
Also published as: CN117635791A

Abstract

The invention discloses a 3D model presentation method and system, a storage medium and electronic equipment, wherein the method comprises the following steps: after the 3D model is subjected to blocking processing, corresponding light field data are generated and optimized; analyzing the light field data, and determining a compression scheme and a decompression scheme according to an analysis result; according to the compression scheme and the decompression scheme, the development of a real-time light field rendering engine is completed; on the premise of meeting preset requirements, deploying and integrating the real-time light field rendering engine into a target platform or system; the real-time rendering engine is used for continuously monitoring and continuously optimizing, and is used for completing the presentation of the 3D modeling, so that the accurate, efficient and real-time rendering display of the complex 3D model is realized, the requirements of different platforms and systems are met, and further, the stability and high performance of the rendering process are ensured while rich and real visual experience is provided.

Description

3D model presentation method and system, storage medium and electronic equipment

Technical Field

The invention relates to the field of three-dimensional modeling rendering, in particular to a 3D model presentation method and system, a storage medium and electronic equipment.

Background

With the rapid development of three-dimensional modeling and virtual reality technology in recent years, 3D models are widely used in various application fields, from film special effects, video games to product design and cultural heritage protection. Traditional 3D model rendering technology mainly relies on basic principles of computer graphics, and achieves a sense of reality visual effect by simulating an interaction process of light and an object. However, with the increase of the complexity of the model and the gradual increase of the requirements of users on the rendering quality and real-time performance, the traditional rendering technology gradually exposes the limitation thereof.

For fine and complex 3D models, such as customized works of art, ancient works of art, etc., conventional rendering techniques often require a lot of computing resources and time, and it is difficult to meet the real-time rendering requirements.

As 3D models become increasingly complex, the amount of data required is also dramatically increasing, which makes storage and transmission of the models more difficult. Compression and efficient transmission of data is an important issue, especially in environments where network bandwidth is limited or computing resources are limited.

Different rendering engines and platforms often have their specific requirements and limitations, making migration and application of 3D models between different platforms challenging.

Although there are many existing 3D rendering engines, they often lack mechanisms for monitoring and continuous optimization of real-time performance, resulting in performance bottlenecks or stability problems that may occur in practical applications.

These problems directly affect the efficiency and experience of the 3D model in practical applications. Therefore, finding a comprehensive solution, which can efficiently process complex 3D models and ensure the universality and high performance of the complex 3D models in various application environments, has become a problem to be solved in the industry.

Disclosure of Invention

Aiming at the problems caused by limitations of the traditional rendering technology, the invention provides a rendering method and a system of a 3D model, a storage medium and electronic equipment.

A method of rendering a 3D model, comprising the steps of:

after the 3D model is subjected to blocking processing, corresponding light field data are generated and optimized;

Analyzing the light field data, and determining a compression scheme and a decompression scheme according to an analysis result;

according to the compression scheme and the decompression scheme, the development of a real-time light field rendering engine is completed;

on the premise of meeting preset requirements, deploying and integrating the real-time light field rendering engine into a target platform or system;

And continuously monitoring and continuously optimizing the real-time rendering engine, wherein the real-time rendering engine is used for completing the presentation of the 3D modeling.

Preferably, the partitioning process includes:

optimizing 3D model data for reducing data complexity of a 3D model, wherein the 3D model data comprises: information data of geometry, texture and color of the 3D model;

dividing the optimized 3D model data into a plurality of model data blocks.

Preferably, the generating and optimizing the corresponding light field data includes:

identifying the characteristics of each model data block through a computer vision algorithm, and obtaining a characteristic data set of the 3D model, wherein the characteristic data set comprises: geometric features, texture features, and illumination features;

analyzing the characteristic data set, and determining a light field model according to an analysis result;

Inputting the model data blocks into a light field model to obtain light field data corresponding to each model data block;

and reducing redundancy of the light field data to finish optimization.

Preferably, the analyzing the optical field data, and determining the compression scheme and the decompression scheme according to the analysis result includes:

Respectively carrying out data characteristic analysis, application scene analysis, data complexity analysis, visual importance analysis and data scale analysis on the light field data, wherein each analysis result corresponds to one compression scheme to be determined;

establishing a candidate compression scheme pool based on all analysis results;

distributing corresponding weights to each analysis result according to preset requirements;

evaluating the compression schemes to be determined in the compression scheme pool according to the weights corresponding to the analysis results, and respectively calculating corresponding comprehensive utility values;

and determining a compression scheme and a decompression scheme corresponding to the compression scheme according to the comprehensive utility value.

Preferably, the developing the real-time light field rendering engine according to the compression scheme and the decompression scheme includes:

through each analysis result, the infrastructure design of the real-time light field rendering engine is completed;

developing a data decompression and loading module by completing the determined compression scheme and decompression scheme;

The development of a real-time rendering algorithm is completed through the optimized light field data;

and optimizing the real-time rendering algorithm through the data decompression and loading module.

Preferably, the deploying and integrating the real-time light field rendering engine into a target platform or system comprises:

Deploying the optimized real-time rendering engine to a target platform or system;

Integrating the real-time rendering engine into an existing workflow of a target platform or system;

and testing the compatibility of the real-time rendering engine and other components in the target platform or system, and performing fine adjustment on the real-time rendering engine based on the actual running environment and the use requirement.

Preferably, performance data of the real-time rendering engine is continuously monitored, an analysis report is periodically generated, and the real-time rendering engine is optimized based on the analysis report.

A presentation system of a 3D model, comprising:

the light field data generation unit is used for generating and optimizing corresponding light field data after the 3D model is subjected to blocking processing;

The compression selection unit is used for analyzing the light field data and determining a compression scheme and a decompression scheme according to an analysis result;

The development unit is used for completing development of the real-time light field rendering engine according to the compression scheme;

the application unit is used for deploying and integrating the real-time light field rendering engine into a target platform or system on the premise of ensuring that preset requirements are met;

and the performance monitoring unit is used for continuously monitoring and optimizing the real-time rendering engine, and the real-time rendering engine is used for completing the presentation of the 3D modeling.

A storage medium having stored thereon a computer program which when executed by a processor realizes the steps of a rendering method of the 3D model.

An electronic device includes a processor and a storage medium;

The storage medium is used for storing instructions; and

The processor is configured to operate according to the instructions to perform the steps of the rendering method of the 3D model.

Compared with the prior art, the invention has the advantages that:

(1) Through continuous monitoring and targeted optimization, the running performance and stability of the real-time rendering engine are obviously improved, rendering tasks of the 3D model can be processed more efficiently, the blocking and delay are reduced, and the user experience is improved;

(2) The potential problems and the optimization direction are found in a large amount of performance data by adopting a data analysis and machine learning algorithm, so that the optimization decision is more accurate and intelligent;

(3) The invention can adapt to 3D model rendering tasks with various types and scales, is easy to be deployed and integrated in different platforms or systems, and has high flexibility and expansibility;

(4) When complex 3D models, such as artwork models, are processed, fine and accurate rendering is achieved, delay in the rendering process is reduced, and browsing and interaction experience of end users is greatly improved.

Drawings

FIG. 1 is a schematic flow chart of the method of the present invention;

FIG. 2 is a flow chart of generating and optimizing corresponding light field data in the present invention;

FIG. 3 is a flow chart of determining a compression scheme and a decompression scheme according to the present invention;

FIG. 4 is a flow diagram of the development of a real-time light field rendering engine in accordance with the present invention;

FIG. 5 is a flow diagram of real-time light field rendering engine deployment and integration in accordance with the present invention;

Fig. 6 is a block diagram of the system of the present invention.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

Some of the block diagrams and/or flowchart illustrations are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, when executed by the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). Additionally, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon, the computer program product being for use by or in connection with an instruction execution system.

A method of rendering a 3D model, comprising the steps of:

Preferably, the partitioning process includes:

dividing the optimized 3D model data into a plurality of model data blocks.

The chunking of a 3D model refers to decomposing a complete 3D model into smaller data chunks or sub-models. The main purpose is to simplify the computational complexity and to increase the processing efficiency. This involves geometric and graphical methods to determine how to divide the model, such as decomposing the model into smaller, manageable and manageable blocks using spatial segmentation algorithms (e.g., octree or KD-tree).

Efficient partitioning should ensure that each data block is balanced as much as possible in size, shape and information contained, and that dependencies between different data blocks are minimized for parallel processing.

The 3D model data optimization is used for reducing data complexity and improving calculation and transmission efficiency.

After the blocking processing, the data optimization reduces the calculation complexity of real-time rendering and saves the storage space; by dividing into multiple data blocks, the parallel computing capabilities of modern hardware, such as the multi-line Cheng Xuanran capabilities of a GPU, can be exploited.

In one embodiment, assume that a fine China porcelain 3D model is rendered:

Geometric optimization: a higher precision model is used for the body of the porcelain to show the fine pattern, while a lower precision model is used for the base portion or interior.

Texture optimization: ensuring that the exquisite painting on the surface of the porcelain remains clear at all observation angles.

Dividing data blocks: the main body, the cover, the handle and the like of the porcelain can be divided into blocks for independent processing and rendering, and the accurate calculation of illumination and shadows of different parts is particularly focused.

And (3) illumination optimization: the gloss part of the porcelain was calculated by detailed illumination, and the shadow part or the bottom was moderately simplified.

Preferably, as shown in fig. 2, the generating and optimizing the corresponding light field data includes:

By computer vision techniques, features of the model data block are accurately captured and encoded.

determining a light field model according to the analysis result:

1. Analyzing 3D model features

Understanding features of the 3D model: the feature data set is studied to understand the spatial structure, optical characteristics and performances of the 3D model expressed by the feature data set under different light conditions.

2. Theoretical investigation of light field model

Understanding the existing light field model: the theoretical basis, the practicability and the performance of the existing light field model under specific scenes (such as complex illumination, shielding, reflection and the like) are researched.

Feature matching: comparing the 3D model characteristics with the performances and adaptability of different light field models, and selecting a model meeting the requirements.

3. Selecting existing models or designing new models

Selecting a model: and if the light field model meeting the requirements exists, selecting an optimal model to generate the light field data of the next step.

Designing a model: if the existing model cannot meet the requirement, a new light field model is considered to be designed, which comprises determining the data structure, coding, storage and sampling strategies of the light field and the like.

4. Light field model verification

Prototype development: based on the selected or designed light field model, a prototype or simulation is created for verifying the effect of the model.

Model verification: in an actual 3D model scene, the light field model is used for simulation and test, and the accuracy and feasibility of the light field model are verified.

Optimizing a model: and carrying out necessary optimization and correction on the light field model according to the verification result.

5. Outputting the final light field model

Document record: the theoretical basis, structure and implementation method of the light field model are recorded, and document support is provided for subsequent development and application.

Model output: and outputting the finally determined light field model data structure and parameters as the basis for generating and encoding the subsequent light field data.

When the model is designed, the universality and the specificity of the model are weighed, so that the model can be well performed in various scenes, and meanwhile, the light field of a specific 3D model can be accurately expressed; ensuring that the calculation and storage efficiency of the light field model meets the actual application requirements so as to support real-time or near real-time rendering; the model ensures that the illumination effect of the 3D model can be accurately reproduced without sacrificing the quality of the light field while ensuring the efficiency.

The light field model determines the generation and coding modes of the subsequent light field data, and further influences the quality and efficiency of light field rendering. It is therefore very critical to ensure that the selected or designed light field model matches the features of the 3D model, while also meeting the requirements of the subsequent steps.

The light field model needs to be built by comprehensively considering information of various characteristic data, such as geometric shapes, textures, illumination and the like. This information is converted into light field data, including the direction, color, intensity, etc. of the light sources, which need to be modeled in detail to achieve a realistic 3D model presentation.

After the light field model processing is carried out on each block of model data, corresponding light field data can be obtained, and the data comprises visual information of the 3D model under different observation angles. Light field data needs to find a balance between high accuracy and computational efficiency to guarantee rendering effects and performance.

And reducing redundancy of the light field data to finish optimization.

Redundancy reduction involves data compression and simplification. In light field rendering, certain data (e.g., data of an invisible area in most observations) is considered redundant. The compression algorithm and the storage format of the light field data need to be carefully selected to achieve the optimization effect while retaining critical visual information.

Through the above process, it is expected that a light field rendering model with higher calculation and storage efficiency while maintaining a high degree of realistic rendering effect can be realized. The method not only can provide rich and dynamic 3D model visual experience, but also can reduce the hardware performance requirement to a certain extent, so that the model can be displayed on more platforms.

In one embodiment, consider an artwork-an ancient copper 3D model:

A. feature extraction

The geometric, texture and illumination characteristics of the copper are extracted by using a computer vision method, such as curves, textures and colors of the copper.

B. Light field model determination

The characteristic data is analyzed to create a model that describes the illumination and color of the copper at different viewing angles, such as the specific reflection of light from the copper at specific angles.

C. Light field data generation

And applying a light field model to each data block of the model to obtain the visual performance data of each block under different visual angles.

D. Data optimization

Light field data that does not play a significant role at most perspectives is identified and pruned while guaranteeing rendering effects at the primary perspectives.

preferably, as shown in fig. 3, the analyzing the optical field data, and determining the compression scheme and the decompression scheme according to the analysis result includes:

by analyzing the light field data from different dimensions, the knowledge of the intrinsic characteristics, application scene adaptability, complexity, visual importance, scale and the like of the light field data can be obtained. For example, data profiling may be implemented by Principal Component Analysis (PCA).

Wherein, the data characteristic analysis results:

1. high frequency data enrichment

The corresponding scheme is as follows: wavelet transform or other compression techniques that can effectively process high frequency information are employed.

Low frequency data enrichment

The corresponding scheme is as follows: consider a compression method based on a predictive model to reduce data redundancy by predicting neighboring values.

2. Application scene analysis results

Real-time requirements are strict

The corresponding scheme is as follows: compression algorithms with low computational complexity and high decompression speed are preferentially considered, and some compression ratios are sacrificed.

Strict requirements for image quality

The corresponding scheme is as follows: with high fidelity compression algorithms, the computational complexity may be high, but the image distortion may be kept low.

3. Data complexity analysis results

Large data volume

The corresponding scheme is as follows: with lossy compression, emphasis is placed on compression ratio, and model simplification, hierarchical data representation can also be considered.

The data volume is smaller

The corresponding scheme is as follows: allowing lossless compression or slightly lossy compression to be used, preserving more detailed information.

4. Visual importance analysis results

The critical visual areas are very prominent

The corresponding scheme is as follows: with regional compression strategies, critical regions are compressed with high fidelity, and non-critical regions can be compressed with a greater proportion.

All areas are of relatively uniform visual importance

The corresponding scheme is as follows: with a uniform compression strategy, the overall data uses a similar compression ratio.

5. Data size analysis results

The spatial resolution is very high

The corresponding scheme is as follows: viewpoint-based selective rendering and data transmission are introduced, and high resolution data is transmitted and processed only when necessary.

The time dimension data is larger

The corresponding scheme is as follows: the correlation of the previous and subsequent frames is utilized to reduce the amount of data, taking into account temporal data prediction and interpolation techniques.

Establishing a candidate compression scheme pool based on all analysis results;

Different analysis dimensions lead to different compression schemes, and a compression scheme pool integrates the schemes to provide a comprehensive alternative scheme set; and combining the schemes derived from the analysis results, and considering the advantages and disadvantages of the schemes under different scenes and purposes.

Ensuring that each analysis dimension is properly considered according to the importance of each analysis dimension under specific application scenes and requirements; relying on user experience or utility analysis based on previous projects.

The quantitative evaluation of the advantages and disadvantages of the schemes is realized, the optimal comprehensive performance of the selected compression scheme and decompression scheme is ensured, the implementation way comprises an algorithm model, such as a utility function model, and the calculation of the comprehensive utility value depends on the weight and the performance of each scheme under each analysis dimension.

According to the compression scheme, efficient data compression and decompression services can be provided according to specific application scenes and requirements; ensuring that a proper balance is achieved between analysis dimensions, such as between compression ratio and decompression mass/speed.

In one embodiment, consider 3D model light field data for a complex artwork (e.g., porcelain):

A. Analyzing dimensions

The feature analysis focuses on fine textures and colors of the porcelain surface; the visual importance analysis focuses on its contribution to the overall visual effect.

B. Compression scheme pool

For example, one approach to compression of high fidelity details is a candidate due to the significance of fine textures on visual effects.

C. Weight allocation

In an application scenario dedicated to online presentation, the fast decompression scheme weight is increased.

D. Comprehensive utility value calculation and scheme determination

And calculating the comprehensive utility value of each scheme according to the preset weight, for example, under the scene of emphasizing the visual effect, the high-fidelity detail compression scheme obtains a higher comprehensive utility value.

The real-time rendering algorithm aims at being capable of efficiently rendering a light field representation conforming to visual perception within a defined time range.

Before the algorithm starts rendering, necessary preprocessing is carried out on the input light field data, so that the rendering speed is increased; appropriate data structures (e.g., BVH tree, octree, etc.) are employed to manage the light field data, reducing the computational complexity of data lookup or traversal.

The parallel computing capacity of the modern GPU is fully utilized, and a plurality of rendering tasks or data units are processed simultaneously; an approximation or simplification model is adopted to simulate the propagation effect of light in a scene, and the rendering precision and efficiency are balanced.

Invoking a hardware-level optimization library, such as CUDA or OpenCL, to further improve the operation efficiency of the algorithm; memory occupation and data exchange frequency of the algorithm in the running process are reduced as much as possible.

By reasonably utilizing the principle, the rendering efficiency can be obviously improved on the premise of ensuring the rendering quality of the light field, and the real-time or near real-time rendering effect can be achieved.

In one embodiment, it is contemplated to process a complex artwork 3D model (e.g., an antique vase model with a very high number of polygons):

A. Light field data management

In the rendering process, the whole 3D space is divided into different blocks by a space division technology, and the light field data in each block are organized together, so that localized rendering calculation is facilitated.

B. algorithm efficiency optimization

Some physical-based rendering methods (PBR) are introduced to simulate light propagation and interaction on the vase surface, such as considering diffuse reflection, specular reflection, etc.

Photon mapping or radiance caching techniques are used to simulate the global brightness effects of light while minimizing the computational effort of the algorithm.

C. Utilizing hardware resources

Multiple light sources and reflective surfaces on the vase model may be processed in parallel by the multithreading capability of the GPU, each thread processing the light field data of one local region, and finally merging the results together.

And the frequently used light field data is stored in the local memory of the GPU, so that the transmission times and delay of the data between the GPU and the CPU are reduced.

Preferably, as shown in fig. 4, the developing the real-time light field rendering engine according to the compression scheme and the decompression scheme includes:

In the design of the infrastructure, the following factors need to be considered: including organization, storage, transmission, rendering, etc. of data.

After the architecture is designed, the system is ensured to process the compressed light field data in a real-time scene, and meanwhile, efficient decompression, loading and the like are supported.

The data decompression and loading module is used for realizing efficient conversion from compressed data to renderable data; it is necessary to perform according to a previously determined compression/decompression scheme to ensure that the light field data can be rapidly and accurately parsed and loaded.

The real-time rendering algorithm is used for efficiently generating visual presentation of the light field; it is necessary to rely on Graphics Processing Unit (GPU) acceleration and achieve seamless interfacing with the aforementioned decompression loading module.

Optimization further reduces the time delay of rendering, improves visual quality, etc., through optimization at an algorithm level, such as enhancing rendering effects or reducing computational burden by utilizing various light field rendering techniques or graphics algorithms.

The invention ensures that the rendering process can be presented within an acceptable time range; despite the data compression, the visual effect of the rendering result can be ensured.

In some cases, a hybrid use of multiple compression schemes may also be used. For example, one compression method may be used in some areas or contexts, while another may be used in other areas or contexts. Such mixed use can better adapt to different application scenarios and data characteristics.

In one embodiment, it is assumed that there is a complex 3D artwork model (e.g., a finely engraved jade):

A. Infrastructure design

An efficient data pipeline needs to be designed, so that compressed light field data can be guaranteed to be quickly transmitted to a rendering module.

B. Data decompression and loading module

It must be ensured that the light field data of the jawarmers can be decompressed rapidly in real-time scenes without causing significant visual degradation.

C. real-time rendering algorithm

The fine detail of the jade is accurately rendered, for example, a rendering effect with strong sense of reality is generated by adapting the characteristic luster and texture characteristics of the fine detail.

D. Optimization of real-time rendering algorithms

For example, by LOD (Level of Detail) techniques, for the level of detail of the observation distance adaptation model, high detail is shown near, while detail is reduced far but the general shape and light shadow are preserved to save computational resources.

preferably, as shown in fig. 5, the deploying and integrating the real-time light field rendering engine into a target platform or system includes:

Confirming whether the architecture of the rendering engine matches the target platform, for example, considering CPU/GPU architecture, operating system, dependency library, etc.; ensuring that the computing, storage and network resources of the target platform meet the requirements of the rendering engine.

Understanding the existing workflow of the target platform, such as data input, processing, output, etc., and determining how the rendering engine is embedded; ensuring that the rendering engine provides enough APIs or interfaces to adapt the existing workflow.

Testing the compatibility of the real-time rendering engine and other components in the target platform or system, and performing fine adjustment on the real-time rendering engine based on the actual running environment and the use requirement;

Verifying whether the rendering engine can work cooperatively with other components of the platform; under the actual hardware and operating environment, evaluating the performance of the rendering engine; and adjusting parameters or algorithms of the engine according to the test feedback.

The deployment and integration of the real-time light field rendering engine to the target platform can enable the real-time rendering of the 3D model to be realized, and the graphic rendering capability of the real-time light field rendering engine is embodied in practical application, so that the required rendering effect and data are provided for users or other system modules.

In one embodiment, consider the development of a 3D artwork display system that includes the display of a fine 3D scanned ceramic vase model:

A. Deploying a real-time rendering engine

On a Web-based 3D exhibition platform, the rendering engine can be packaged into WebAssembly modules, which are deployed on a server side or a client side in combination with WebGL technology to provide real-time 3D model browsing experience for users.

B. integration into existing workflows

In the workflow of uploading, previewing, editing, exposing, etc. the 3D model, the rendering engine needs to be embedded at an appropriate stage. For example, after a user uploads a 3D model file, a rendering engine calculates and generates a preview in advance at a server side, and then provides a real-time interactive 3D model browsing function at a browser side of the user.

C. testing and trimming

And carrying out load test, and observing the performance and resource occupation condition of the rendering engine when a plurality of users browse the 3D model on line at the same time.

And according to the hardware performance, the network bandwidth and the actual feedback of user interaction, the rendering algorithm or parameters are moderately adjusted, so that smooth experience at different user ends is ensured.

Continuously collecting key performance index data when the rendering engine runs, such as frame rate, resource occupation (CPU, GPU, memory), network delay and the like; and transmitting the collected performance data to a data center or a cloud end in real time or periodically for storage and analysis.

Extracting key information and potential problems from a large amount of performance data by adopting a data analysis and machine learning algorithm; and generating an intuitive report according to the data processing result, and displaying the running condition and the performance bottleneck of the rendering engine.

By analyzing the report, locating performance problems or potential optimization points of the rendering engine, adjusting algorithm or module implementation, testing and implementing optimization for the located problems.

By continuous monitoring and optimization, the performance and stability of the rendering engine are continuously improved, and the method can be better suitable for various workloads and changes of user demands.

In one embodiment, assume that there is a 3D model presentation of an ancient bronze in an online museum:

A. Performance monitoring

The implementation is as follows: and (3) implementing a performance monitoring system to monitor information such as frame rate, delay and the like of a plurality of users when browsing the 3D model of the ancient bronze ware.

Scene: performance is of close concern in multi-user simultaneous access or specific hardware environments.

B. Analysis report

The object is: and analyzing the data of the high access time period to find out the source of the performance bottleneck or delay.

The implementation is as follows: the performance data is visualized and reports generated using a data analysis tool, such as TensorBoard or DataDog.

C. Optimized implementation

Problems: it is assumed that the frame rate of the rendering engine is significantly reduced at high concurrent accesses found in the report.

Optimizing: the optimization direction includes reducing the number of polygons of the model, optimizing the light shadow algorithm, or utilizing a more efficient data compression approach, etc., for performance bottlenecks.

Through continuous monitoring, analysis and optimization, the real-time rendering engine can more stably and smoothly serve users of the online museums, and a high-quality 3D model browsing experience is provided.

As shown in fig. 6, a presentation system of a 3D model includes:

The light field data generating unit divides a complex 3D model (such as a piece of exquisite handicraft) into a plurality of data blocks, and each block is processed respectively so as to generate and optimize light field data with finer granularity; the light field data of each model data block is generated and optimized through the computer vision algorithm processing of the geometric, texture and illumination characteristics of the model data block, so that the vision quality of the model can be maintained while the data quantity is reduced.

The light field data generating unit can greatly reduce the required processing capacity and the memory consumption, especially when processing complex models.

For example, for an ancient jade 3D model, it is first segmented into smaller parts (e.g., tool parts, engraving parts, etc.), and then the light field data is extracted separately.

The compression selection unit evaluates the utility of different compression schemes by using various analysis methods (data feature analysis, application scene analysis and the like) respectively, and finds out the optimal compression and decompression schemes by a weighted sum method.

The compression selection unit ensures that the light field data can be compressed and transmitted efficiently under the premise of ensuring the quality under different application scenes.

The development unit develops a real-time rendering engine capable of rapidly decompressing and loading the light field data according to the compression scheme determined as described above.

The development unit ensures that the 3D model can be smoothly rendered even in an environment where computing resources such as mobile devices are limited.

The application unit deploys the rendering engine to the target platform and ensures that it can integrate seamlessly with other systems or workflows on the platform.

The application unit enables the 3D model to be presented on a variety of platforms, whether Virtual Reality (VR) environments or web platforms.

The performance monitoring unit collects and analyzes performance data (such as rendering delay, memory occupation, etc.) of the rendering engine in real time or periodically, and then continuously optimizes the engine according to analysis results.

The performance monitoring unit ensures that even after the engine is on line, the user experience and the system stability can be improved through continuous optimization.

An electronic device includes a processor and a storage medium;

The storage medium is used for storing instructions; and

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory includes volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for rendering a 3D model, comprising the steps of:

and continuously monitoring and optimizing the rendering engine, wherein the real-time rendering engine is used for completing the presentation of the 3D modeling.

2. The method of presenting a 3D model according to claim 1, wherein the chunking process comprises:

dividing the optimized 3D model data into a plurality of model data blocks.

3. The method of rendering a 3D model of claim 2, wherein the generating and optimizing the corresponding light field data comprises:

and reducing redundancy of the light field data to finish optimization.

4. The method for presenting a 3D model according to claim 1, wherein analyzing the light field data and determining a compression scheme and a decompression scheme according to an analysis result comprises:

establishing a candidate compression scheme pool based on all analysis results;

5. The method of claim 4, wherein the developing the real-time light field rendering engine according to the compression scheme and the decompression scheme comprises:

6. The method of rendering a 3D model of claim 1, wherein the deploying and integrating the real-time light field rendering engine into a target platform or system comprises:

7. The method of claim 1, wherein the real-time rendering engine performance data is continuously monitored and an analysis report is periodically generated, the real-time rendering engine being optimized based on the analysis report.

8. A system for rendering a 3D model, comprising:

The performance monitoring unit is used for continuously monitoring and optimizing the real-time rendering engine, and the real-time rendering engine is used for completing the presentation of the 3D modeling.

9. A storage medium having stored thereon a computer program, characterized in that the program, when being executed by a processor, realizes the steps of the rendering method of a 3D model according to any of claims 1-7.

10. An electronic device, comprising a processor and a storage medium;

The storage medium is used for storing instructions; and

The processor is operative according to the instructions to perform the steps of the method of rendering a 3D model as claimed in any one of claims 1 to 7.