Point Cloud Encoding for Planar Nodes Using Neighbor Occupancy
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Solution Overview
Problem
The challenge of efficiently encoding and decoding point cloud data, particularly for nodes with planar characteristics, is exacerbated by the inefficient predictive encoding of planar structure information, leading to poor performance in current point cloud compression methods.
Innovation Solution
The proposed solution involves determining neighboring nodes of a current node and performing predictive encoding or decoding based on occupancy information of these nodes, utilizing planar encoding techniques to improve efficiency for nodes with planar characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If planar encoding is used for nodes with planar characteristics, then encoding efficiency is improved, but predictive encoding performance remains poor due to insufficient reference information
Solution Approach 1:
The patent performs preliminary identification of planar nodes before encoding, using occupancy information from neighboring nodes to predict planar structure characteristics. This preliminary action enables the system to prepare appropriate encoding strategies in advance, improving both encoding efficiency and predictive performance by ensuring that planar nodes are correctly identified and handled with suitable reference information before the actual encoding process begins.
Solution Approach 2:
The patent employs feedback mechanisms by utilizing occupancy information from neighboring nodes to continuously refine predictions of planar structure information. The decoded occupancy information from surrounding nodes feeds back into the prediction process, allowing the system to adapt and improve predictive accuracy dynamically during encoding and decoding operations, thereby resolving the contradiction between encoding efficiency and predictive performance.
2Measurement precision
If more neighboring nodes are used for predictive encoding, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by selectively using occupancy information from neighboring nodes based on their relevance to the current node being encoded. Rather than uniformly processing all neighboring nodes, the system adapts the prediction process to local characteristics, using only the necessary subset of neighboring information required for accurate planar structure prediction. This approach maintains high prediction accuracy while avoiding unnecessary computational overhead from processing redundant or less relevant neighboring data.
Data Source
AI summary
The present disclosure provides point cloud encoding and decoding methods, which include: determining N neighboring nodes of a current node, and performing encoding and decoding on the planar structure information of the current node based on occupancy information of the N neighboring nodes.