Mesh Zippering for Gap-Free 3D Patch Reconstruction
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Solution Overview
Problem
Current methods for compressing 3D meshes in volumetric content, such as point clouds, lack an efficient mechanism to transmit connectivity information and are not suitable for sparse meshes, leading to gaps and inefficiencies in mesh reconstruction.
Innovation Solution
Implementing mesh zippering techniques to modify vertex positions at patch borders, using various methods to ensure neighboring patches align without gaps, and adaptively selecting zippering implementations based on detected criteria to improve mesh reconstruction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If V-PCC projection method is used to compress point clouds, then compression efficiency is improved, but the method cannot transmit connectivity information required for 3D mesh compression
Solution Approach 1:
The patent segments the transmission process into two distinct parts: first transmitting geometry data using V-PCC projection method for efficient compression, then separately transmitting connectivity information using mesh compression techniques. This segmentation allows each data type to be optimized independently, resolving the contradiction between compression efficiency and connectivity information transmission.
Solution Approach 2:
The patent merges V-PCC projection-based geometry encoding with mesh compression-based connectivity encoding into a unified transmission framework. By combining these two approaches, the system achieves both high compression efficiency from V-PCC and complete connectivity information from mesh compression, eliminating the limitation of using either method alone.
2Adaptability or versatility
If RAW patch data is used to encode sparse point clouds, then encoding flexibility is improved, but encoding efficiency deteriorates and attribute data may be missing
Solution Approach 1:
The patent applies different encoding strategies to different parts of the data: V-PCC projection is used for geometry data where compression efficiency is critical, while mesh compression is used for connectivity data where flexibility and completeness are important. This local quality approach optimizes each segment according to its specific requirements, resolving the contradiction between flexibility and efficiency.
3Productivity
If mesh vertices are encoded using V-PCC followed by mesh compression, then compression is improved for dense meshes, but the method fails for sparse meshes
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the system to switch between different encoding approaches based on mesh density characteristics. For dense meshes, the traditional V-PCC followed by mesh compression approach is used; for sparse meshes, alternative methods are employed. This dynamic adaptation resolves the contradiction between compression performance and mesh density versatility.
Data Source
AI summary
Ways to improve mesh reconstruction by modifying the position of vertices at the border of patches to make sure that neighboring patches do not have a gap between them, also known as zippering, are described herein. Six different methods to implement the post-processing operation, as well as syntax elements and semantics for transmission of the filter parameters, are disclosed. A hierarchical method indicate the geometry distortion that can generate gaps between patches. The value per frame, or per patch, or per boundary object is sent. The number of bits to encode the values is also dependent on the previous geometry distortion. A method sends index matches instead of geometry distortion. The matching index is sent per boundary vertex, but a method to send only one index of the pair is implemented as well.