3D Point Cloud Compression Using Octree Entropy Coding
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Solution Overview
Problem
Processing and storing large-scale 3D point cloud data is computationally expensive and inefficient, particularly for consumer devices with limited resources, and existing compression methods do not effectively manage the size and complexity of point clouds for various applications.
Innovation Solution
A learned deep entropy model over a tree structure, such as an octree, is used for lossless compression of point cloud data, incorporating local and global neighborhood information, and employing adaptive entropy coding to facilitate parallel processing and reduce resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional compression methods are used for point cloud data, then device complexity is reduced, but compression performance and detail preservation deteriorate
Solution Approach 1:
The point cloud data is divided into multiple blocks or regions, and each block is processed independently through the neural network. This segmentation allows parallel processing, reducing computational complexity while maintaining high compression performance through localized detail preservation
Solution Approach 2:
A neural network model acts as an intermediary between the raw point cloud data and the compressed representation. The neural network learns optimal compression transformations, achieving high compression ratios while preserving essential geometric details that traditional methods would lose
2Measurement precision
If high-resolution point cloud data is processed, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The neural network is pre-trained on large datasets to learn optimal compression and reconstruction transformations. During actual processing, the pre-trained network rapidly compresses and reconstructs point cloud data, maintaining high precision while minimizing processing time through efficient inference
Solution Approach 2:
The system processes only the most critical geometric features and attributes at full precision, while less important details are compressed with lower precision. This selective processing maintains measurement precision for essential elements while reducing overall computational burden
3Loss of information
If more computational resources are allocated to compression, then compression ratio is improved, but device complexity and resource requirements worsen
Solution Approach 1:
The neural network model uses learnable parameters that are optimized during training to achieve high compression ratios. Once trained, the fixed parameters enable efficient compression with minimal computational resources during deployment, as the heavy optimization work was performed offline during the training phase
Data Source
AI summary
A method, apparatus or system for processing point cloud information can involve a learned deep entropy model over octrees for lossless compression/decompression of 3D point cloud data, wherein self-supervised compression/decompression involves an adaptive entropy coder operating on a tree-structured conditional entropy model and utilizing information from the local neighborhood as well as the global topology from the tree structure.