3D Textured Mesh Compression With Base Mesh and Displacement Fields
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video-based solutions for compressing 3D volumetric data, such as V3C, struggle to efficiently encode connectivity information in 3D meshes, leading to inefficiencies in compression and reconstruction.
Innovation Solution
A method for compressing 3D textured meshes by pre-processing to generate a base mesh and displacement field, using quantization, wavelet transforms, and video encoding to create a compressed bitstream, while leveraging static or motion encoders for optimized encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If V3C video-based solutions are used to compress 3D volumetric data, then compression of projected 2D data is achieved, but connectivity information in 3D meshes cannot be efficiently encoded
Solution Approach 1:
The mesh compression is divided into two independent parts: connectivity information (encoded separately using mesh-specific encoding) and geometric/attribute data (encoded using V3C video-based compression). This segmentation allows each component to be optimized independently, resolving the contradiction between compression efficiency and connectivity preservation.
Solution Approach 2:
A parameterization mapping serves as an intermediary between the 3D mesh connectivity and the 2D video encoding system. The parameterization transforms the 3D mesh surface into a 2D parameter domain, enabling the use of efficient 2D video coding techniques while preserving the original 3D connectivity structure through the mapping relationship.
2Device complexity
If mesh connectivity is encoded using existing video coding technologies, then compression is simplified, but compression efficiency and quality deteriorate
Solution Approach 1:
The encoding system is segmented into a specialized mesh encoder for connectivity information and a video encoder for geometric data. This division allows the mesh encoder to use optimized algorithms for connectivity while the video encoder handles the geometric data using well-established efficient techniques, overall improving compression efficiency without excessive complexity.
Solution Approach 2:
The method changes the representation parameters of mesh data by transforming it into a parameterized 2D domain. This parameter transformation enables the use of efficient video coding techniques while maintaining the essential 3D mesh information, thereby improving compression efficiency without requiring complex custom encoding for all components.
3Device complexity
If mesh data is compressed without adaptive tessellation, then processing is simplified, but visual fidelity and quality are reduced
Solution Approach 1:
The mesh tessellation is made dynamic and adaptive rather than fixed. The system automatically adjusts the level of detail and tessellation density based on the content importance and compression requirements, allowing simplified processing for low-fidelity regions while maintaining high visual fidelity for critical areas, thus balancing complexity and quality.
Data Source
AI summary
A method of compressing a 3D textured mesh M(i), the 3D textured mesh being defined by connectivity C(i), geometry G(i), texture coordinates T(i), and texture connectivity CT(i), wherein the mesh is associated with one or more 2D image attribute maps A(i) describing attributes associated with the mesh surface, can include pre-processing 3D textured mesh M(i) and attribute maps A(i) to generate a base mesh m(i) and displacement field d(i); and processing 3D textured mesh M(i), attribute maps A(i), base mesh m(i), and the displacement field d(i) to generate a compressed bitstream b(i).