Dynamic Mesh Segmentation for Real-Time Remeshing Compression
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Solution Overview
Problem
Existing mesh compression standards do not effectively handle time varying attribute maps and connectivity information in dynamic meshes, leading to large file sizes and reliance on manual processing, which hinders real-time capture and transmission.
Innovation Solution
A method involving rendering meshes from virtual camera views, using a fully-convolutional model for real-time segmentation, generating pixel-wise masks, and performing remeshing and mesh optimization to reduce redundancy and bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mesh compression standards (IC, MESHGRID, FAMC) are used for dynamic meshes, then compression is achieved, but they cannot handle time varying attribute maps and connectivity information
Solution Approach 1:
The patent segments the mesh data into multiple components: geometry data, attribute maps, and connectivity information. Each component is processed and compressed separately, allowing the system to handle time-varying attributes and connectivity while maintaining compression efficiency. The segmentation enables independent optimization of each data type.
Solution Approach 2:
The patent implements dynamic adaptation by detecting changes in attribute maps and connectivity information over time. When changes are detected, the system dynamically adjusts the compression strategy, updating only the changed portions rather than re-compressing the entire mesh, thus handling time-varying data efficiently.
2Manufacturing precision
If uncompressed or raw mesh and texture data of dynamic human subjects and objects are used, then high quality is maintained, but file sizes become prohibitively large
Solution Approach 1:
The patent applies different compression strategies to different regions of the mesh based on their importance and characteristics. Critical regions with high detail requirements maintain higher quality with less compression, while less critical areas use more aggressive compression, optimizing the overall quality-to-size ratio.
Solution Approach 2:
The system dynamically adjusts compression parameters such as quantization levels, precision, and detail thresholds based on the specific mesh data, its intended use, and storage/transmission constraints. This allows flexible optimization between quality and file size for different applications.
3Manufacturing precision
If manual processing is used for mesh optimization, then optimization quality can be high, but real-time capture to transmission pipelines are prevented
Solution Approach 1:
The patent implements automated mesh optimization algorithms that perform remeshing, decimation, and attribute optimization without manual intervention. The system automatically analyzes the mesh structure, identifies optimization opportunities, and applies appropriate transformations, enabling real-time processing while maintaining quality through algorithmic decision-making.
Solution Approach 2:
The system performs preliminary mesh optimization during the capture phase, preparing the mesh data in advance for efficient compression and transmission. By pre-processing the mesh to remove redundancies and optimize structure before compression, the system reduces the computational load during real-time transmission.
Data Source
AI summary
A method and apparatus that renders a mesh from one or more virtual camera views of an object, the one or more virtual camera views being projections of the mesh onto a plurality of 2D planes; inputs the one or more virtual camera views into a fully-convolutional model for real-time segmentation; generates one or more 2D pixel-wise masks based on the fully-convolutional model, the 2D pixel-wise masks being associated with one or more segmented elements; performs remeshing and mesh optimization using the one or more 2D pixel-wise masks; and generates an output mesh based on the remeshing and mesh optimization.