Polygonal Mesh Quality Assessment via Triangle Sampling
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Solution Overview
Problem
Current point-based quality evaluation methods for meshes are limited in their ability to handle polygonal meshes with more than three edges and are sub-optimal in converting triangle meshes to point clouds, leading to inaccurate and computationally inefficient quality metrics.
Innovation Solution
A method that converts polygonal meshes into triangle meshes through subdivision, samples points on these triangle faces, and computes geometry and attribute distortions using point clouds to assess quality, employing MSE, PSNR, SSIM, or MS-SSIM metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point-based sampling metric is used for quality evaluation, then the method is simple and fast, but it only applies to triangle meshes without generality for polygonal meshes
Solution Approach 1:
The patent segments polygonal meshes into triangle meshes through systematic subdivision. Each polygon face with more than three edges is divided into multiple triangular faces, allowing the existing efficient triangle mesh processing algorithms to be applied while extending compatibility to general polygonal meshes
Solution Approach 2:
The patent introduces an intermediary conversion step that transforms polygonal meshes into triangle meshes before applying point-based sampling. This intermediary representation enables the use of simple and fast triangle mesh evaluation methods while handling general polygonal mesh structures
2Productivity
If triangle mesh to point cloud conversion is performed, then quality metrics can be computed, but the sampling method may be sub-optimal and loss of geometric detail occurs
Solution Approach 1:
The patent performs preliminary actions by maintaining the original polygonal mesh structure throughout the quality assessment process. Instead of converting to point cloud and losing geometric information, the method computes metrics directly on the polygonal mesh using the subdivided triangle representation, preserving geometric detail while enabling efficient computation
Solution Approach 2:
The patent changes the parameter representation from point cloud coordinates to polygonal mesh geometry with associated attributes. This allows quality metrics to be computed in the original mesh parameter space, avoiding information loss from discretization while maintaining computational efficiency through the triangle subdivision approach
3Device complexity
If quality metrics are computed from converted point clouds, then the process is simplified, but accuracy in correlation to subjective quality decreases
Solution Approach 1:
The patent creates a virtual copy of the triangle mesh structure from the polygonal mesh subdivision, which is then used for metric computation. This virtual copy maintains the geometric relationships and attributes needed for accurate quality assessment while keeping the actual processing operations simple and efficient
Solution Approach 2:
The patent develops a universal quality assessment framework that works for both triangle meshes and polygonal meshes through the subdivision approach. The same metric computation algorithms can be applied universally across different mesh types, simplifying the processing pipeline while maintaining accuracy through the faithful representation of polygonal geometry
Data Source
AI summary
A method including receiving, at an encoder, a first original polygonal mesh and a second distorted polygonal mesh, the first original polygonal mesh being an original polygonal mesh and the second polygonal mesh being a distorted polygonal mesh; converting the first polygonal mesh and the second polygonal mesh into two or more triangle meshes by subdividing the plurality of polygon faces of each of the first polygonal mesh and the second polygonal mesh into a plurality of triangle faces; sampling a plurality of points on each of the plurality of triangle faces from both the first and the second polygonal meshes; generating at least a first sampled point cloud for one of the first or the second polygonal mesh using the sampled plurality of points; and computing a geometry and attribute distortion profile between the first and the second polygonal meshes based on at least the first sampled point cloud.