Hybrid Geometric Coding for Point Cloud Compression
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Solution Overview
Problem
Point clouds require significant data and bandwidth for communication, and existing technologies struggle to balance data reduction with maintaining high quality, as networks often operate at or near bandwidth capacity.
Innovation Solution
The implementation of hybrid geometric coding that dynamically chooses between octree and quadtree encoding based on a flatness parameter or rate distortion, allowing for efficient data transmission by switching between 3D and 2D encoding methods to optimize data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If octree encoding is used for point cloud compression, then data reduction is achieved, but encoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple octants (8 sub-regions) within each octree node, allowing parallel processing and independent encoding of each octant. This segmentation reduces the overall encoding complexity by breaking down the large-scale compression task into smaller, manageable units that can be processed efficiently
Solution Approach 2:
The encoding approach dynamically adapts between different octree traversal modes (visiting all 8 octants vs. visiting only occupied octants) based on the actual data distribution. This dynamic adaptation optimizes the balance between compression ratio and encoding complexity for different point cloud configurations
2Productivity
If quadtree encoding is used for point cloud compression, then encoding speed improves, but compression efficiency decreases
Solution Approach 1:
The patent merges the advantages of both octree and quadtree encoding by implementing a hybrid approach where quadtree-based 2D projection provides fast encoding speed, while selective octree-based 3D subdivision is applied to specific regions to improve compression efficiency. This combination achieves both high encoding speed and good compression ratio
Solution Approach 2:
Different encoding strategies are applied to different regions of the point cloud based on local characteristics. Regions with high point density or complex geometry use octree encoding for better compression, while regions with simpler structures use quadtree encoding for faster processing, optimizing the overall balance between speed and compression
3Manufacturing precision
If higher point cloud quality is demanded, then customer satisfaction improves, but bandwidth requirements increase
Solution Approach 1:
The patent transitions between 3D octree encoding and 2D quadtree projection, utilizing dimensional transformation to achieve efficient compression. The 2D projection reduces data redundancy by representing 3D point cloud information in a 2D space, thereby reducing bandwidth requirements while maintaining quality
Solution Approach 2:
The encoding precision and level of detail are dynamically adjusted based on the desired quality output and available bandwidth. By changing parameters such as octree depth, projection resolution, and point sampling density, the system can adaptively optimize the balance between quality and bandwidth consumption
Data Source
AI summary
An apparatus comprises a processor configured to obtain a point cloud, make a choice between octree encoding or quadtree encoding of a parent node, and encode the parent node into a bitstream based on the choice; and a transmitter coupled to the processor and configured to transmit the bitstream. An apparatus comprises a receiver configured to receive a bitstream; and a processor coupled to the receiver and configured to parse from the bitstream a first parameter indicating a depth of PCC, decode first nodes before the depth using octree decoding, parse from the bitstream a second parameter indicating a node coding mode for second nodes at and after the depth, and decode the second nodes based on the second parameter.