Point Cloud Tetris Packing for Temporal Consistency
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Solution Overview
Problem
Current point cloud compression methods for dynamic 3D scenes in VR/AR applications lack temporal consistency in patch allocation, leading to decreased compression efficiency due to inefficient placement of 3D patches in 2D canvases across frames.
Innovation Solution
The method of point cloud global tetris packing performs patch matching to identify temporally consistent matches, generates a double linked list, calculates an accumulated occupancy map, and uses a novel weight calculation to prioritize and sort patches for consistent allocation in the 2D canvas, ensuring that patches maintain their 3D position across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video coding with patch matching is used for point cloud compression, then compression efficiency is improved, but temporal consistency in patch allocation deteriorates
Solution Approach 1:
The patent performs preliminary patch matching and sorting before the actual encoding process. By pre-calculating the optimal patch allocation order using a sorting algorithm that considers run lengths and patch positions, the system establishes a consistent temporal framework in advance, which then guides the encoding process to maintain temporal consistency across frames.
Solution Approach 2:
The patent introduces dynamic patch sorting that adapts to the specific characteristics of each frame sequence. The sorting algorithm dynamically adjusts patch ordering based on run lengths, patch positions, and temporal relationships, allowing the system to optimize for both compression efficiency and temporal consistency according to the actual content being encoded.
2Productivity
If patch matching is performed for temporal coding, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies patch matching selectively based on local characteristics. By analyzing run lengths and patch positions, the system identifies regions where patch matching will be most beneficial and focuses computational effort there, rather than uniformly processing all patches. This local quality approach optimizes the ratio between compression gain and computational cost.
Solution Approach 2:
The patent changes the parameters used for patch comparison and matching, using simplified metrics based on run lengths and patch positions rather than full pixel-level comparison. This parameter transformation reduces the computational complexity of patch matching while maintaining sufficient accuracy for effective temporal coding.
3Speed
If patches are allocated without temporal consistency, then encoding speed is improved, but space usage efficiency in 2D canvas deteriorates
Solution Approach 1:
The patent performs preliminary sorting of patches based on their temporal and spatial characteristics before allocation to the 2D canvas. By pre-establishing the optimal allocation order using run length encoding and patch position analysis, the system ensures that patches are placed in an order that maximizes space utilization while maintaining temporal consistency, without requiring complex real-time adjustments during encoding.
Data Source
AI summary
A method of mapping 3D point cloud data into 2D surfaces for further efficient temporal coding is described herein. Point cloud global tetris packing utilizes 3D surface patches to represent point clouds and performs temporally consistent global mapping of 3D patch surface data into 2D canvas images.