Hybrid-Tree Coding for Point Cloud Inter and Intra Prediction
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Solution Overview
Problem
Existing point cloud compression methods, such as octree coding and prediction-tree coding, are not optimized for both dense and sparse point clouds, leading to inefficiencies in data representation and transmission.
Innovation Solution
A hybrid-tree coding method that combines octree coding and predictive coding, allowing for enhanced inter/intra prediction at the block level. This method determines an octree for splitting the space containing the point cloud, generates predictions using intra or inter prediction, and codes syntax elements to indicate the prediction mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If octree coding is used for dense point clouds, then spatial partitioning efficiency is improved, but data representation efficiency deteriorates for sparse point clouds
Solution Approach 1:
The patent applies dynamics by making the coding structure adaptable rather than fixed. The hybrid tree structure dynamically switches between octree-based partitioning for dense regions and predictive coding for sparse regions, allowing the system to optimize performance based on local point cloud characteristics rather than using a single static coding approach throughout
Solution Approach 2:
The patent implements local quality by applying different coding strategies to different regions of the point cloud based on their local characteristics. Dense regions use octree coding while sparse regions use predictive coding, allowing each local area to be processed with the most appropriate method for its specific properties
2Loss of information
If prediction-tree coding is used for sparse point clouds, then data representation efficiency is improved, but spatial partitioning efficiency deteriorates for dense point clouds
Solution Approach 1:
The system dynamically adapts the coding approach based on local density characteristics. The hybrid tree structure allows switching between predictive coding and octree coding depending on whether the local region is sparse or dense, optimizing both data representation and spatial partitioning for each region
Solution Approach 2:
Different coding methods are applied to different local regions based on their properties. Sparse regions benefit from predictive coding while dense regions benefit from octree partitioning, ensuring each region is processed with the method best suited to its characteristics
3Device complexity
If a single coding method is used for all point cloud densities, then device complexity is reduced, but coding efficiency deteriorates
Solution Approach 1:
The patent merges two previously separate coding approaches (octree coding and predictive coding) into a unified hybrid tree structure. This combination allows the system to leverage the strengths of both methods while maintaining a coherent overall framework, achieving improved coding efficiency without requiring entirely separate processing systems
Data Source
AI summary
A device for decoding a bitstream that includes point cloud data is configured to determine an octree that defines an octree-based splitting of a space containing the point cloud, wherein a leaf node of the octree contains one or more points of the point cloud; and directly decode positions of each of the one or more points in the leaf node, wherein to directly decode the positions of each of the one or more points in the leaf node, the one or more processors are further configured to: generate a prediction of the one or more points; and determine the one or more points based on the prediction.