Adaptive UV-Atlas Sampling for 3D Mesh Compression
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Solution Overview
Problem
Existing 3D media processing technologies face challenges in efficiently compressing and transmitting large 3D models due to their significant data requirements, which impact storage and transmission resources.
Innovation Solution
Adaptive sampling techniques are applied to mesh frames, determining region-specific sampling rates and generating 2D maps for encoding, with non-overlapping configurations and predictive encoding of sampling rates and offsets to optimize compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform sampling is applied to the entire mesh frame, then the encoding process is simple, but the compression efficiency is poor and data volume remains large
Solution Approach 1:
The patent divides the mesh frame into multiple regions with different characteristics and applies different sampling rates to each region. Important regions with high visual impact use lower sampling rates to preserve quality, while less important regions use higher sampling rates to reduce data volume. This local differentiation resolves the contradiction by optimizing both compression efficiency and visual quality where needed.
Solution Approach 2:
The patent dynamically determines sampling rates based on region characteristics such as curvature, visual importance, and geometric complexity. The sampling rate is not fixed but adapts to the local properties of each region, allowing the system to achieve better compression efficiency while maintaining necessary quality in critical areas.
2Manufacturing precision
If higher sampling rates are applied to important regions, then the visual quality is improved, but the data volume increases
Solution Approach 1:
The patent identifies important regions based on visual impact metrics and applies higher sampling rates only to those specific regions rather than uniformly across the entire mesh. This localized approach ensures visual quality is improved where it matters most while keeping data volume controlled in less critical regions.
Solution Approach 2:
The patent changes the sampling rate parameter dynamically based on region importance and visual characteristics. By adjusting this key parameter locally rather than globally, the system achieves improved visual quality in important regions without proportionally increasing overall data volume.
3Productivity
If region-specific sampling rates are determined, then the compression efficiency is improved, but the encoding complexity increases
Solution Approach 1:
The patent segments the mesh frame into multiple regions with distinct characteristics and processes each region independently with its own sampling rate. This segmentation allows the system to achieve better compression efficiency through targeted sampling while managing encoding complexity by breaking down the overall task into smaller, more manageable regional processing steps.
Solution Approach 2:
The patent introduces region-specific sampling rate parameters that change based on local mesh characteristics. While this increases encoding complexity compared to uniform sampling, the automation of parameter selection based on objective criteria (visual importance, curvature, etc.) manages the complexity increase while delivering significant compression efficiency improvements.
4Manufacturing precision
If the UV atlas is densely sampled, then the texture quality is maintained, but the storage and transmission resources are consumed
Solution Approach 1:
The patent applies different sampling densities to different regions of the UV atlas based on their visual importance and geometric complexity. Regions that require high texture quality (such as areas with fine geometric details or high visual impact) are sampled densely, while less critical regions are sampled more sparsely, optimizing the balance between texture quality and resource consumption.
Data Source
AI summary
In some examples, an apparatus for mesh coding includes processing circuitry. The processing circuitry receives a data structure for a mesh frame with polygons representing a surface of an object. The data structure for the mesh frame includes a UV atlas that associates vertices of the mesh frame to UV coordinates in the UV atlas. The processing circuitry determines respective sampling rates for regions of the mesh frame according to respective characteristics of the regions of the mesh frame and applies, on the UV atlas, the respective sampling rates for the regions of the mesh frame to determine sampling positions on the UV atlas. The processing circuitry generates one or more sampled two dimensional (2D) maps for the mesh frame according to the sampling positions on the UV atlas, and encodes the one or more sampled 2D maps into a bitstream.