Topology-Aware Surface Tracking for Consistent 3D Tessellation
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Solution Overview
Problem
Inconsistent tessellation of moving/animated 3D objects across frames in video capture leads to difficulties in data compression, color grading, and visual effects, as traditional methods result in unrelated triangle connectivity and lack of surface correspondences.
Innovation Solution
The method involves selecting keyframes based on a scoring metric, calculating a transformation field to transform keyframe meshes into each mesh, and substituting transformed keyframes for original meshes, achieving consistent tessellation through topology-aware surface tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If surface reconstruction algorithm is run independently at each frame, then processing speed is improved, but tessellation consistency deteriorates
Solution Approach 1:
The frame sequence is divided into keyframes and non-keyframes. Only keyframes undergo surface reconstruction, while non-keyframes use transformation fields for approximation. This segmentation allows parallel processing of keyframes while maintaining tessellation consistency through the transformation approach for intermediate frames.
Solution Approach 2:
Transformation fields are pre-calculated between keyframes to establish the mapping relationships. These pre-computed transformation fields are then applied to non-keyframes to generate consistent tessellation without running full surface reconstruction, thus maintaining both speed and consistency.
2Stability of the object's composition
If keyframe substitution is applied to all frames, then tessellation consistency is improved, but computational complexity increases
Solution Approach 1:
Different processing approaches are applied to different frames based on their characteristics. Keyframes receive full surface reconstruction with high computational resources, while non-keyframes use the more efficient transformation field approach. This local differentiation optimizes the balance between consistency and computational complexity.
Solution Approach 2:
Instead of performing full surface reconstruction on every frame, the patent creates transformed copies of keyframe meshes using pre-calculated transformation fields. These copied and transformed meshes serve as approximations for non-keyframes, reducing computational complexity while maintaining visual consistency.
3Measurement precision
If error measurement is performed on every transformed keyframe, then approximation accuracy is improved, but processing time increases
Solution Approach 1:
Error measurement is performed selectively rather than on every transformed keyframe. The patent applies error measurement to validate transformation quality at critical points or thresholds, rather than exhaustively checking every single frame. This partial verification maintains sufficient accuracy while reducing processing time.
Data Source
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AI summary
Consistent tessellation via topology-aware surface tracking is provided in which a series of meshes is approximated by taking one or more meshes from the series and calculating a transformation field to transform the keyframe mesh into each mesh of the series, and substituting the transformed keyframe meshes for the original meshes. The keyframe mesh may be selected advisedly based upon a scoring metric. An error measurement on the transformed keyframe exceeding tolerance or threshold may suggest another keyframe be selected for one or more frames in the series. The sequence of frames may be divided into a number of subsequences to permit parallel processing, including two or more recursive levels of keyframe substitution. The transformed keyframe meshes achieve more consistent tessellation of the object across the series.