Adaptive Octree Coding for Planar Point Cloud Nodes
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Solution Overview
Problem
Existing geometry-based point cloud compression (G-PCC) methods do not effectively utilize geometric distribution characteristics for planar nodes, leading to low coding efficiency.
Innovation Solution
Implement partitioning processing to determine node groups and use mode flag information for adaptive coding, including predictive coding of planar nodes based on geometric distribution characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If adaptive determination of planar coding is performed using only distribution density of nodes in each level, then the coding process is simple, but geometry coding efficiency is low
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different node types within the same level. Instead of uniformly applying planar coding based solely on overall distribution density, the invention identifies and separately processes planar nodes versus non-planar nodes. This allows the coding process to adapt locally to the geometric characteristics of each node, improving overall coding efficiency while maintaining procedural simplicity through clear classification rules.
2Stability of the object's composition
If planar coding is performed on all nodes in each level based on distribution density, then coding consistency is maintained, but coding efficiency for planar nodes is not optimized
Solution Approach 1:
The patent implements segmentation by dividing nodes into distinct categories: planar nodes and non-planar nodes. This segmentation allows different coding strategies to be applied to different node types. Planar nodes receive specialized planar coding treatment that exploits their geometric properties, while non-planar nodes use standard coding methods. This segmentation maintains consistency within each category while optimizing efficiency for planar structures.
3Measurement precision
If detailed geometric distribution characteristics are considered for planar node identification, then coding precision is improved, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by performing planar node identification before the main coding process. The method pre-processes the point cloud data to classify nodes as planar or non-planar based on geometric distribution characteristics. This preliminary classification enables the subsequent coding stage to efficiently apply appropriate methods without repeatedly analyzing geometric properties during coding, thus reducing overall processing complexity while maintaining high identification accuracy.
Data Source
AI summary
Embodiments of the present disclosure provide a decoding method. A decoder determines nodes in a level obtained after octree partitioning as one node group; decodes a bitstream to determine mode flag information corresponding to a current node group among at least one node group; and determines a prediction value of a node in the current node group according to a decoding mode indicated by the mode flag information.