Point Cloud Octree Encoding With Planar Neighbor Context
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Solution Overview
Problem
Current point cloud compression methods, such as VPCC and GPCC, face inefficiencies in data reduction and are not well-suited for large-scale point clouds, particularly in applications like autonomous driving where high compression ratios are needed without compromising data quality.
Innovation Solution
The method employs an octree-based structure for point cloud representation, utilizing planar context information from neighboring nodes to enhance entropy encoding, which improves data reduction by providing more reliable predictions about occupancy patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional entropy encoding is used for point cloud compression, then the encoding process is simple, but the compression ratio is insufficient
Solution Approach 1:
The patent performs preliminary determination of occupancy patterns for parent nodes and pre-calculates planar context information from neighboring nodes before the actual entropy encoding process. This advance preparation enables more efficient compression during encoding by having context information ready, resolving the contradiction between compression ratio and encoding complexity
Solution Approach 2:
The patent introduces planar context information as an intermediary element that mediates between the raw occupancy data and the entropy encoding process. This intermediary provides additional spatial context that improves compression efficiency without requiring fundamental changes to the encoding architecture, thus improving compression ratio while controlling complexity
2Quantity of substance
If more context information is used for entropy encoding, then the compression ratio improves, but the computational complexity increases
Solution Approach 1:
The patent applies planar context information locally to specific child nodes based on their spatial relationships with neighboring parent nodes. Rather than processing all nodes uniformly with full context information, the method selectively applies context based on local spatial characteristics, improving compression where beneficial while minimizing unnecessary computational overhead
Solution Approach 2:
The patent segments the point cloud space into parent nodes and child nodes with specific spatial relationships. By dividing the encoding process into segments that can independently utilize planar context information from neighboring nodes, the method achieves better compression through localized context usage while distributing computational load efficiently
Data Source
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AI summary
A method for encoding and decoding, an encoder and decoder for a point cloud is disclosed. The method for encoding a point cloud to generate a bitstream of compressed point cloud data, wherein the point cloud's geometry is represented by an octree-based structure with a plurality of nodes having parent-child relationships by recursively splitting a volumetric space containing the point cloud into sub-volumes each associated with a node of the octree-based structure, comprises: Determining an occupancy pattern for a parent node based on the occupancy of its child nodes; Determining a planar context information for at least one of the child nodes, wherein the planar context information of a present child node is determined according to the occupancy pattern of the present parent node and the occupancy pattern of at least one neighboring parent node positioned directly next to the present parent node; and Entropy encoding/decoding the occupancy pattern parent node based on the determined planar context information to produce encoded/decoded data for the bitstream.