Point Cloud Encoding with Sparsity-Based Isolated Point Switching
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Solution Overview
Problem
In dense point clouds, nodes with only one point are rare, leading to inefficient compression in the isolated point encoding mode, resulting in decreased performance in the AVS point cloud encoding process.
Innovation Solution
Determine the sparsity level of a target point cloud and enable an isolated point encoding mode for sparse point clouds that satisfy specific conditions, such as a sum of Morton code bits exceeding a predetermined factor of minimum edge lengths, indicating sparse and isolated points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If isolated point encoding mode is enabled for all nodes, then encoding simplicity is improved, but compression efficiency deteriorates for dense point clouds
Solution Approach 1:
The patent applies local quality by differentiating encoding strategies based on point cloud density characteristics. Dense regions use octree-based encoding while sparse/isolated regions use isolated point encoding. The sparsity level determination module identifies regions with different density characteristics and applies appropriate encoding methods to each, thereby optimizing both compression efficiency and encoding simplicity where applicable.
Solution Approach 2:
The patent changes the encoding parameter dynamically based on sparsity level. When sparsity level indicates sparse regions and isolated point conditions are met, the system switches from octree encoding to isolated point encoding mode. This parameter change allows the system to adapt to different point cloud characteristics and resolve the contradiction between encoding simplicity and compression efficiency.
2Loss of substance
If octree-based encoding is used for dense point clouds, then compression efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent segments the point cloud encoding process into two distinct paths based on sparsity level: octree-based encoding for dense regions and isolated point encoding for sparse regions. The sparsity level determination module acts as a segmentation controller that divides the encoding task according to point cloud characteristics, allowing each method to operate in its optimal domain.
Solution Approach 2:
The patent applies partial action by using octree-based encoding only for dense point cloud regions where it provides compression benefits, rather than applying it universally. The system performs octree encoding partially - only when sparsity level indicates dense regions - thereby reducing overall encoding complexity while maintaining compression efficiency where needed.
3Productivity
If sparsity level determination is added, then encoding performance is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by determining sparsity level at the beginning of the encoding process, before actual encoding occurs. The sparsity level determination module performs preliminary analysis of point cloud characteristics (such as calculating average points per node or density metrics) to classify the point cloud type. This preliminary classification enables the system to select the appropriate encoding strategy in advance, improving overall encoding performance while minimizing additional processing time through efficient pre-analysis.
Data Source
AI summary
This application discloses a point cloud encoding processing method and apparatus and a point cloud decoding processing method and apparatus. The point cloud encoding processing method of embodiments of this application includes: determining sparsity level information of a target point cloud; and in a case that the sparsity level information indicates the target point cloud being a sparse point cloud and a node to be encoded corresponding to the target point cloud satisfies an isolated point encoding condition, enabling an isolated point encoding mode.