Point Cloud Compression Using Hybrid Deep Entropy Coding
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Solution Overview
Problem
Point cloud data, particularly dynamic point clouds from 3D LiDAR sensors, pose challenges in efficient compression and processing due to their large size and real-time transmission requirements in applications like autonomous driving and VR/AR, where existing methods struggle to balance compression efficiency and computational cost.
Innovation Solution
The proposed solution involves a method for encoding and decoding point cloud data using a tree structure-based approach, combining neural network modules for feature extraction and prediction, and entropy coding techniques to generate a bitstream, which allows for efficient compression and reconstruction of point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network modules are used for feature extraction and prediction in point cloud compression, then compression efficiency is improved, but computational cost increases
Solution Approach 1:
The neural network processing is divided into multiple stages: a point-based neural network module processes local point features, while a voxel-based neural network module processes global spatial context. This segmentation allows the system to handle computational complexity in manageable portions, improving compression efficiency while controlling overall computational cost through hierarchical processing.
Solution Approach 2:
The system transitions from processing only point-level data to incorporating voxel-based spatial context, adding a dimensional perspective that captures global structure information. This dimensional expansion enables more accurate occupancy prediction and compression without requiring excessive computational resources, as the voxel representation provides compact global context.
2Measurement precision
If tree structure-based compression with neural networks is applied, then data quality is maintained, but device complexity increases
Solution Approach 1:
The compression system is segmented into distinct functional modules: a point-based neural network module for local feature extraction, a voxel-based neural network module for global context, and an entropy coding module for compression. This modular segmentation maintains data quality through specialized processing while reducing overall system complexity by making each component independently optimizable and easier to implement.
Solution Approach 2:
The voxel representation serves as an intermediary between the raw point cloud data and the final compressed output. It provides a compact intermediate representation that captures global spatial context without requiring complex direct processing of all point data, thereby maintaining data quality while simplifying the overall processing architecture.
3Speed
If real-time compression is performed on dynamic point clouds, then transmission speed is improved, but processing time increases
Solution Approach 1:
The processing is segmented into parallel operations: point-based feature extraction can be performed independently on each point or small groups of points, while voxel-based context processing operates on the aggregated spatial structure. This segmentation enables parallel computation that maintains real-time transmission speed while reducing total processing time through concurrent operations.
Solution Approach 2:
The system performs preliminary voxelization and global context computation before the actual compression encoding. This preliminary action prepares spatial context information in advance, allowing the main compression process to proceed faster without requiring complex real-time calculations, thus maintaining transmission speed while reducing processing time.
Data Source
AI summary
Methods and apparatuses for decoding and encoding point cloud data are described herein. A method may include accessing point cloud data compressed based on a tree structure. The method may further comprise fetching points in a neighborhood associated with a current node of the tree structure, and computing a feature using a point-based neural network module, based on three-dimensional (3D) locations of the fetched points. The method may include predicting, using a neural network module, an occupancy symbol distribution for the current node based on the feature, and determining the occupancy for the current node from the encoded bitstream and the predicted occupancy symbol distribution. The method may include computing another feature using a convolution-based neural network module, based on a voxelized version of the fetched points, and fusing the feature and the another feature with one or more known features of a current node to compose a comprehensive feature.