Neural Network Probability Prediction for Point Cloud Entropy Coding
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Solution Overview
Problem
Current methods for compressing 3D point cloud data, such as those from LiDAR sensors, are inefficient due to the large volume of data generated, which poses challenges for storage and real-time communication in applications like autonomous vehicles and immersive media.
Innovation Solution
A neural network-based method for point cloud data compression that utilizes pre-trained neural networks to determine probabilities for entropy coding, selecting the appropriate network based on the level of the node in the tree representation, and incorporating spatial and semantic context information for effective compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression algorithms are used for point cloud data, then device complexity is low, but compression efficiency is insufficient leading to large data volume
Solution Approach 1:
The patent replaces conventional mechanical compression algorithms with a neural network-based system. The neural network processes point cloud data to generate probability predictions for entropy coding, achieving superior compression efficiency. The system substitutes traditional signal processing methods with learning-based approaches that adapt to the statistical characteristics of point cloud data.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on the learned characteristics of point cloud data. The neural network adapts probability models according to different data regions and configurations, changing parameters like entropy coding probabilities and partitioning strategies to optimize compression for each specific input.
2Adaptability or versatility
If a single neural network is used for all tree levels, then device complexity is low, but adaptability to different data characteristics is poor
Solution Approach 1:
The patent divides the compression task across multiple specialized neural networks, each trained for specific tree levels or data characteristics. Instead of using one generic network, the system segments the problem and applies dedicated networks to different segments (tree levels), allowing each network to specialize in the statistical patterns of its assigned level.
Solution Approach 2:
Different neural networks are deployed for different tree levels based on the local characteristics of data at each level. The system applies local quality by tailoring the network architecture and training data to match the specific properties of each tree level, improving adaptability to local data patterns.
Data Source
AI summary
A point cloud data coding method and related coding devices are provided. The method comprises: obtaining an N-ary a tree representation of point cloud data; determining probabilities for entropy coding of information associated with of a current node of the tree, including: selecting a neural network, out of two or more pretrained neural networks, according to a level of the current node within the tree, obtaining the probabilities by processing input data related to the current node by the selected neural network; and entropy coding of the information associated with the current node using the determined probabilities.