Hybrid Point Cloud Compression Framework
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Solution Overview
Problem
Existing point cloud compression methods are inefficient for large-scale point clouds, particularly in real-time applications like autonomous driving and virtual reality, due to high computational costs and limited compression ratios.
Innovation Solution
A hybrid coding framework that employs different decoding strategies for various bit levels of point cloud data, utilizing tree-based, voxel-based, and point-based methods to optimize compression and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single uniform decoding strategy is used for all bit levels, then the implementation is simple, but the compression efficiency is insufficient for large-scale point clouds
Solution Approach 1:
The patent segments the bit levels of point cloud data into different subsets, applying different decoding strategies to each subset. Specifically, it divides bit levels into multiple groups and uses distinct decoding approaches for each group, allowing optimized processing for different data characteristics while maintaining overall system efficiency.
Solution Approach 2:
The patent applies local quality by tailoring the decoding strategy to the specific characteristics of each bit level subset. Different regions of the data (different bit levels) receive customized decoding treatment based on their local properties, improving overall compression efficiency without requiring complex global optimization.
2Quantity of substance
If high compression ratios are achieved through aggressive compression, then the data size is reduced, but the computational cost increases
Solution Approach 1:
The patent segments the decoding process into multiple stages corresponding to different bit level subsets. This allows the system to process data in manageable chunks with different computational requirements, reducing peak computational cost while achieving high compression ratios through cumulative processing.
Solution Approach 2:
The patent employs dynamic decoding strategies that adapt to the characteristics of different bit level subsets. The decoding approach changes dynamically based on the data being processed, optimizing the balance between compression ratio and computational cost for each segment rather than using a static high-cost approach throughout.
Data Source
AI summary
In one implementation, we propose a hybrid architecture to compress and decompress a point cloud. In particular, a first decoding block is for the most significant bits, typically coded by a tree-based coding method. A second decoding block is for the middle-range of bits, typically coded by a voxel-based method. A third decoding block is for the least significant bits, typically coded by a point-based method. For example, the decoder configures the decoder's network according to the total number of bits and the bit partitioning positions; decodes a coarse point cloud and its associated point-wise features using a tree-based decoding block; upsamples the coarse point cloud to a denser one and updates the point-wise features using a voxel-based decoding block; and refines the precision of the coordinates of the dense but low bit depth point cloud to high bit depth point cloud using a point-based decoding block.