V-PCC Segmentation Refinement Focused on Edge Cube Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing grid-based refining method for segmentation in video-based point cloud compression (V-PCC) requires high computation and memory usage due to the need to update projection plane indices for all points, leading to inefficiencies.
Innovation Solution
The method focuses on refining only the grids containing edge portions by using a grid segmentation unit, edge cube search unit, surrounding cube search unit, smooth score calculation unit, and projection plane index update unit to calculate and update projection plane indices only for edge cubes and their surrounding cubes, thereby reducing unnecessary computation and memory access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If projection plane indices are updated for all points in grid-based segmentation refinement, then segmentation accuracy is improved, but computation and memory usage increase significantly
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different spatial regions. Edge cubes (containing segment boundaries) receive full refinement with projection plane index updates, while non-edge cubes use simplified inheritance from parent nodes. This localized differentiation maintains segmentation accuracy at boundaries while reducing computation in homogeneous regions.
Solution Approach 2:
The patent segments the refinement process into two distinct paths: edge cube refinement and non-edge cube refinement. This segmentation allows the system to apply computationally intensive operations only where necessary (at segment boundaries) while using lighter operations in other regions, thereby resolving the contradiction between accuracy and computational cost.
2Reliability
If all grids are refined in V-PCC segmentation, then image quality is maintained, but processing speed decreases
Solution Approach 1:
The patent extracts and identifies edge cubes containing segment boundaries, then applies full refinement only to these extracted regions. Non-edge cubes are excluded from intensive refinement and instead inherit properties from parent nodes, thereby maintaining image quality at critical boundaries while improving overall processing speed.
Solution Approach 2:
The patent implements partial action by applying full refinement selectively only to edge cubes rather than all cubes. This partial application of the refinement operation maintains necessary image quality at segment boundaries while avoiding excessive computation in regions where full refinement is not critical.
Data Source
AI summary
The present disclosure relates to an apparatus and method for fast refining segmentation for a V-PCC encoder. The apparatus may include a grid segmentation unit segmenting a coordinate space of a point cloud into grid units, and an edge cube search unit searching a cube containing one or more points among the cubes segmented into grid units and containing a segment boundary. The apparatus may also include a surrounding cube search unit searching an edge surrounding cube containing one or more points within a predetermined range from the edge cube, and a smooth score calculation unit calculating smooth scores for all the edge surrounding cubes and all the edge cubes. The apparatus may further include a projection plane index update unit obtaining a normal score based on the calculated smooth scores and updating a projection plane index of each point in the edge cube using the normal score.