Point Cloud Entropy Coding with Neighbor-Guided Occupancy Prediction
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Solution Overview
Problem
Current point cloud compression methods are inefficient and lack effective encoding and decoding processes, particularly for large datasets used in applications like autonomous vehicles and virtual reality, where rapid and accurate data compression is crucial.
Innovation Solution
The method involves encoding and decoding point clouds using a tree structure with recursive splitting of sub-volumes, entropy encoding based on probability distributions selected from neighboring nodes, and updating these distributions to optimize compression, allowing for efficient generation and reconstruction of compressed bitstreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy coding methods are used for point cloud compression, then the coding complexity remains low, but the compression efficiency is insufficient
Solution Approach 1:
The patent implements dynamic probability distribution selection based on occupancy patterns. Instead of using a fixed probability distribution, the system adaptively selects from multiple probability distributions (e.g., geometric, binomial, negative binomial) depending on the local occupancy characteristics of the point cloud data. This dynamic adaptation improves compression efficiency by matching the coding strategy to the actual data distribution, while the complexity increase is managed through efficient selection mechanisms.
Solution Approach 2:
The patent changes the parameter of probability distribution selection based on occupancy patterns. By monitoring occupancy data from neighboring nodes and adjusting the probability distribution parameters accordingly, the system optimizes the entropy coding process. This allows the encoder to adapt to varying point densities and spatial distributions, achieving better compression ratios without requiring fundamentally complex architectural changes.
2Productivity
If probability distribution selection based on neighboring nodes is implemented, then compression performance improves by 4-20%, but the coding complexity increases
Solution Approach 1:
The patent performs preliminary analysis of occupancy patterns from neighboring nodes before entropy coding the current node. By pre-determining the appropriate probability distribution based on neighboring occupancy data, the system prepares the optimal coding parameters in advance. This preliminary action allows the main coding process to proceed efficiently with minimal runtime complexity, achieving the 4-20% compression improvement while managing the overall complexity burden.
Solution Approach 2:
The system uses feedback from neighboring node occupancy data to inform the probability distribution selection for the current node. This feedback mechanism allows the encoder to leverage spatial correlations in the point cloud data, where the occupancy characteristics of adjacent regions provide valuable information for predicting and efficiently coding the current region. The feedback loop improves compression by exploiting these spatial dependencies without requiring excessive computational resources.
Data Source
AI summary
Methods and devices for encoding a point cloud. A current node associated with a sub-volume is split into further sub-volumes, each further sub-volume corresponding to a child node of the current node, and, at the encoder, an occupancy pattern is determined for the current node based on occupancy status of the child nodes. A probability distribution is selected from among a plurality of probability distributions based on occupancy data for a plurality of nodes neighbouring the current node. The encoder entropy encodes the occupancy pattern based on the selected probability distribution to produce encoded data for the bitstream and updates the selected probability distribution. The decoder makes the same selection based on occupancy data for neighbouring nodes and entropy decodes the bitstream to reconstruct the occupancy pattern.