3D Point Cloud N-Ary Tree Encoding for Adaptive Leaf Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for encoding three-dimensional data face challenges in improving coding efficiency and reducing processing amounts, particularly in the context of point cloud compression.
Innovation Solution
A three-dimensional data encoding method that appends information to a bitstream indicating whether a leaf in an N-ary tree structure includes a single or multiple points, generating and encoding the appropriate tree structure accordingly, and a decoding method that decodes this information to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed N-ary tree structure with single-point leaves is used, then the encoding structure is simple, but coding efficiency deteriorates when multiple points exist in a leaf region
Solution Approach 1:
The patent applies dynamics by making the leaf node structure adaptable rather than fixed. Each leaf node can dynamically adjust between containing a single point or multiple points based on the actual data distribution, allowing the encoding structure to flexibly respond to varying point densities in different spatial regions
Solution Approach 2:
The patent implements local quality by allowing different parts of the N-ary tree to have different leaf configurations. Regions with sparse points use single-point leaves while dense regions use multi-point leaves, optimizing the encoding representation for each local area rather than applying a uniform structure throughout
2Productivity
If a fixed N-ary tree structure with multi-point leaves is used, then coding efficiency improves for dense regions, but processing amount increases unnecessarily for sparse regions
Solution Approach 1:
The patent applies local quality by configuring leaf nodes differently based on local point density. Sparse regions use single-point leaves requiring minimal processing, while dense regions use multi-point leaves that achieve better compression, thus optimizing the balance between coding efficiency and processing amount for each local area
3Productivity
If variable leaf structures are used to improve coding efficiency, then data compression is optimized, but device complexity increases
Solution Approach 1:
The patent uses dynamics to enable leaf nodes to adapt their structure based on point density thresholds. This dynamic adjustment allows the system to achieve variable compression efficiency without requiring a completely different encoding structure for each case, managing complexity through adaptive rules rather than multiple fixed structures
Solution Approach 2:
The patent applies parameter changes by modifying the leaf node configuration parameter (single point vs. multiple points) based on the point density parameter. This allows the encoding structure to adapt to different data characteristics by changing a key parameter rather than redesigning the entire encoding system
Data Source
AI summary
A three-dimensional data encoding method includes: appending, to a bitstream, first information indicating whether a leaf to be included in an N-ary tree structure of three-dimensional points included in three-dimensional data is to include a single three-dimensional point or two or more three-dimensional points, where N is an integer greater than or equal to 2; when the first information indicates that the leaf is to include a single three-dimensional point, generating an N-ary tree structure in which a leaf includes a single three-dimensional point, and encoding the N-ary tree structure; and when the first information indicates that the leaf is to include two or more three-dimensional points, generating an N-ary tree structure in which a leaf includes two or more three-dimensional points, and encoding the N-ary tree structure.