Point Cloud Tree Node-Layer Planar Encoding Eligibility
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Solution Overview
Problem
Current point cloud coding technologies face challenges in efficiently compressing large volumes of point cloud data, leading to high computational complexity and low coding gains due to the need to determine planar encoding/decoding mode eligibility for each node in the tree structure.
Innovation Solution
The method determines planar encoding/decoding mode eligibility at the node-layer level for point cloud geometry information, reducing computational complexity by assessing eligibility based on local occupancy density and proportion of nodes using planar modes, thereby simplifying the encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If planar encoding/decoding mode eligibility is determined for each node in the tree structure, then encoding precision and decoding accuracy are improved, but computational complexity increases
Solution Approach 1:
The patent segments the tree structure into multiple node-layers and determines planar encoding/decoding mode eligibility at the node-layer level rather than for each individual node. This segmentation reduces the computational complexity from O(N) where N is the total number of nodes to O(L) where L is the number of node-layers, while still maintaining sufficient encoding precision and decoding accuracy through layer-based eligibility assessment.
2Productivity
If planar encoding mode is used for all nodes, then coding gain is improved, but adaptability to different point cloud geometries deteriorates
Solution Approach 1:
The patent applies local quality by determining planar encoding mode eligibility separately for each node-layer based on local occupancy density and geometric characteristics. This allows different node-layers to use appropriate encoding modes (planar or non-planar) according to their specific geometric properties, thereby achieving both coding gain for suitable layers and adaptability to diverse point cloud geometries across different layers.
Data Source
AI summary
An encoding method and a decoding method are provided. The encoding method includes the following. A tree structure for geometry information of a point cloud is obtained, where the tree structure has at least two node-layers, and each of the at least two node-layers includes at least one node. Planar-encoding-mode eligibility corresponding to a first node-layer of the tree structure is determined. Whether a first node in the first node-layer is encoded using a planar encoding mode is determined according to the planar-encoding-mode eligibility.