Point Cloud Tree Switching for Sparse and Dense Compression
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Solution Overview
Problem
Current methods for compressing point cloud data are inefficient, particularly in handling the geometry of point clouds, which can be sparsely or densely populated, leading to challenges in coding location and requiring improved encoding and decoding processes for effective storage and transmission.
Innovation Solution
The use of tree-based structures, such as volume trees and predictive-point trees, for encoding and decoding point cloud data, where volume trees recursively divide the volumetric space into sub-volumes with occupancy flags, and predictive-point trees associate points directly or as residual positions relative to ancestor points, allowing for adaptive compression based on density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single tree structure (volume tree or predictive-point tree) is used for encoding point cloud data, then the encoding process is simple, but the compression efficiency is insufficient for point clouds with varying density
Solution Approach 1:
The patent implements dynamic tree switching where the encoder can transition between volume tree and predictive-point tree structures based on the local density characteristics of point cloud data. This dynamic adaptation allows the system to optimize compression efficiency for different regions (sparse vs. dense) while maintaining a relatively simple base encoding framework, thereby improving overall compression performance without excessive complexity increase
Solution Approach 2:
The patent changes the structural parameter of the tree representation by switching between two different tree types (volume tree and predictive-point tree) depending on the density parameter of the point cloud data. This parameter-based adaptation enables the system to adjust its encoding strategy to match the characteristics of the data being compressed, improving efficiency without requiring a completely different encoding system for each case
2Productivity
If volume trees are used to recursively divide volumetric space, then sparse point clouds are encoded efficiently, but densely populated regions require more bits and compression efficiency decreases
Solution Approach 1:
The patent applies different tree structure qualities to different regions of the point cloud data. Volume trees are used for sparse regions where they provide efficient compression, while predictive-point trees are used for dense regions where they reduce the number of bits required. This local adaptation of structure quality allows the system to optimize bit usage for each region's specific characteristics rather than applying a one-size-fits-all approach
3Productivity
If predictive-point trees are used to associate points directly with ancestor points, then densely populated point clouds are encoded efficiently, but sparsely populated regions require more bits and compression efficiency decreases
Solution Approach 1:
The patent applies predictive-point tree structure specifically to dense regions where it excels at compression, while using volume trees for sparse regions. This local quality assignment ensures that each region uses the tree structure best suited to its density characteristics, minimizing the total bits required across the entire point cloud while maintaining high compression efficiency in both sparse and dense areas
4Productivity
If tree switching is implemented to adapt to point cloud density, then compression efficiency is improved, but the complexity of encoding and decoding processes increases
Solution Approach 1:
The patent implements a dynamic switching mechanism that automatically selects between volume tree and predictive-point tree based on measured density characteristics. This dynamic approach improves compression efficiency by adapting to local conditions while keeping the switching logic relatively simple, thereby achieving better compression without proportionally increasing system complexity
Solution Approach 2:
The patent uses feedback from density analysis to control tree selection. By measuring the density characteristics of point cloud regions and using this feedback to determine which tree structure to apply, the system optimizes compression efficiency adaptively. This feedback-based control allows intelligent decision-making without requiring complex manual configuration or excessive computational overhead
Data Source
AI summary
Methods and devices for coding point cloud data using volume trees and predicted-point trees. In one embodiment of the disclosure, a method of encoding a point cloud data to generate a bitstream of compressed point cloud data representing a three-dimensional location of a physical object is provided, the point cloud data being located within a volumetric space. The method includes compressing a first part of the point cloud data represented by a first tree of a first type; determining for a given node of the first tree if an assignation to a second type of tree is enabled, said given node still being processed for the first tree; when the assignation is enabled, compressing a second part of the point cloud data represented by a second tree of the second type wherein, features associated with a root node of the second tree are at least partially obtained from the given node.