Context Modeling Occupancy Coding Point Cloud
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Solution Overview
Problem
Current point cloud compression techniques, such as the Test Model 13 (TMC13) codec, face limitations in flexibility and compression efficiency due to rigid octree partitioning, which may not suit diverse point cloud characteristics and applications, hindering effective data reduction for storage and transmission.
Innovation Solution
The proposed method involves context modeling for occupancy coding using flexible tree partitioning, such as depth-first and breadth-first traversal orders, and distance-based context modeling, allowing for more efficient encoding of occupancy information through a look-up-table and adaptive encoding techniques, enabling hybrid traversal orders and improved coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rigid octree partitioning is used, then the encoding process is simple, but the adaptability to diverse point cloud characteristics is poor
Solution Approach 1:
The patent introduces dynamic and adaptive partitioning strategies that adjust the partitioning structure based on the specific characteristics of the point cloud data. Instead of using a fixed rigid octree structure, the system dynamically selects partitioning methods (e.g., octree, k-d tree, uniform grid) and traversal orders (depth-first, breadth-first, hybrid) tailored to different data distributions and application requirements, thereby improving adaptability while managing complexity through systematic selection criteria.
2Productivity
If rigid octree partitioning is used, then the encoding process is simple, but the compression efficiency is limited
Solution Approach 1:
The patent employs parameter changes by introducing multiple configurable parameters including partitioning method selection, traversal order choices, and context modeling parameters. These parameters are adjusted based on point cloud characteristics to optimize compression efficiency. The system changes partitioning depth, tree structure parameters, and encoding parameters dynamically to achieve better compression ratios while managing encoding complexity through adaptive parameter selection.
Solution Approach 2:
The patent applies segmentation by dividing the point cloud data into different regions and applying different encoding strategies to different segments. The partitioning process segments the 3D space into manageable units, and the traversal order segments the encoding process into phases (e.g., encoding occupied nodes first, then empty nodes). This segmentation allows the system to optimize compression efficiency for different data regions while keeping the overall encoding process organized and manageable.
3Productivity
If distance-based context modeling is not used, then the encoding process is faster, but the compression ratio is lower
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing distance metrics and context information in lookup tables before the actual encoding process. The distance-based context modeling pre-establishes probability models based on spatial relationships, allowing the encoder to quickly reference pre-computed context information during encoding. This preliminary preparation reduces the computational burden during real-time encoding, achieving better compression ratios without proportionally increasing encoding time.
Data Source
AI summary
A method for coding information of a point cloud comprises obtaining the point cloud including a set of points in a three-dimensional space; partitioning the point cloud into a plurality of objects and generating occupancy information for each of the plurality of objects; and encoding the occupancy information by taking into account the distance between the plurality of objects.