3D Point Cloud Branch Encoding for Varying Point Densities
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Solution Overview
Problem
Current methods for encoding three-dimensional data are inefficient, particularly in applications involving large datasets like point clouds, where existing compression techniques do not effectively optimize coding for varying densities and complexities of three-dimensional data structures.
Innovation Solution
A three-dimensional data encoding method that generates an N-ary tree structure, where different encoding processes are applied to branches based on their characteristics, allowing for optimized encoding of first and second branches with distinct node structures and quantization parameters, resulting in improved coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single encoding process is used for all branches of the three-dimensional data structure, then the encoding process is simple and fast, but the coding efficiency is low due to inability to optimize for varying point densities
Solution Approach 1:
The patent divides the three-dimensional data structure into multiple branches of an N-ary tree, where each branch can be encoded independently using different encoding processes. This segmentation allows the system to apply appropriate encoding methods to different regions based on their point density characteristics, thereby improving overall coding efficiency without requiring a completely complex unified approach.
Solution Approach 2:
The patent implements local quality by allowing different encoding processes to be applied to different branches of the N-ary tree structure. Each branch can use encoding parameters and methods optimized for its specific point density and complexity characteristics, rather than applying a uniform encoding approach to the entire dataset. This local optimization resolves the contradiction by improving coding efficiency in high-density regions while maintaining simplicity in low-density regions.
2Productivity
If different encoding processes are applied to each branch of the N-ary tree structure, then the coding efficiency is improved, but the encoding process complexity increases
Solution Approach 1:
The patent introduces dynamic selection of encoding processes based on the characteristics of each branch in the N-ary tree structure. The system can adaptively choose appropriate encoding methods and parameters for each branch based on point density metrics, allowing the encoding process complexity to vary dynamically rather than being statically high for all branches. This resolves the contradiction by making the system flexible and adaptive.
Solution Approach 2:
The patent changes encoding parameters such as quantization precision and encoding method based on the point density and complexity of each branch. By adjusting these parameters locally rather than globally, the system achieves improved coding efficiency where needed while avoiding unnecessary complexity in regions where simpler encoding suffices, thereby resolving the contradiction between efficiency and complexity.
3Adaptability or versatility
If uniform quantization parameters are used across all branches, then the encoding process is simpler, but the ability to manage varying point densities is reduced
Solution Approach 1:
The patent applies local quality by using different quantization parameters for different branches of the N-ary tree structure based on their point density characteristics. High-density branches can use finer quantization to preserve detail, while low-density branches can use coarser quantization to reduce data size. This local parameter adaptation resolves the contradiction by improving density management capability while keeping the overall parameter management systematic rather than chaotic.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting quantization parameters according to the point density and complexity of each branch. This allows the system to adapt to varying data characteristics across different regions of the three-dimensional structure, improving versatility in managing diverse point densities while maintaining manageable complexity through rule-based parameter selection.
Data Source
AI summary
A three-dimensional data encoding method includes: generating an N-ary tree structure of three-dimensional points included in three-dimensional data, where N is an integer greater than or equal to 2; generating first encoded data by encoding a first branch using a first encoding process, the first branch having, as a root, a first node included in a first layer that is one of layers included in the N-ary tree structure; generating second encoded data by encoding a second branch using a second encoding process different from the first encoding process, the second branch having, as a root, a second node included in the first layer and different from the first node; and generating a bitstream including the first encoded data and the second encoded data.