Point Cloud Bitstream Structure for Adaptive Neural Compression
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Solution Overview
Problem
Existing technologies face challenges in efficiently compressing and transmitting massive point cloud data for applications like metaverse and AR/VR, necessitating improved compression methods, especially with the increasing use of spatial scanning sensors in smartphones.
Innovation Solution
A method and apparatus for encoding and decoding point cloud data using artificial intelligence and signal processing, employing a bitstream structure that separates sequence and compression technology information, and utilizes neural networks and signal processing-based codecs for efficient compression and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used for point cloud data, then the compression process is simple, but the compression efficiency is insufficient for massive point cloud data
Solution Approach 1:
The patent segments the compression system into separate functional modules: neural network-based compression for position information and signal processing-based compression for attribute information. This segmentation allows each module to specialize in specific tasks, improving overall compression efficiency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal compression framework that can handle both position information and attribute information through a single bitstream structure. The system integrates multiple compression techniques (neural networks and signal processing codecs) within one unified framework, enabling efficient compression of diverse point cloud data types without requiring separate systems for each data type.
2Measurement precision
If point cloud data is transmitted at high bit rate, then the quality is maintained, but the transmission time and bandwidth consumption increase
Solution Approach 1:
The patent changes the parameters of point cloud data representation by applying neural network-based compression to position information and signal processing compression to attribute information. This transforms the data into a more compact form with reduced bit rate, maintaining quality through intelligent compression algorithms while reducing transmission time and bandwidth requirements.
3Adaptability or versatility
If compression technology information is mixed with sequence information in the bitstream, then the bitstream structure is simple, but the flexibility for adaptive transmission is reduced
Solution Approach 1:
The patent segments the bitstream into distinct parameter sets: SPS (Sequence Parameter Set) for sequence information and GPS (Geometry Parameter Set) for compression technology information. This segmentation enables independent modification and transmission of each type of information, providing adaptability for different transmission conditions while organizing complexity into structured, manageable sections.
Solution Approach 2:
The patent introduces identifier information as an intermediary element that links the SPS and GPS, allowing the decoder to correctly associate and process the separated parameter sets. This intermediary mechanism maintains the structural organization needed for flexible adaptive transmission while enabling the system to adapt to varying transmission requirements without redesigning the entire bitstream structure.
Data Source
AI summary
A method for decoding a point cloud according to a present disclosure, the method comprises: obtaining a bitstream for an encoded point cloud based on a neural network; and reconstructing the point cloud by decoding the bitstream, wherein the bitstream includes an SPS and a GPS, and wherein the GPS includes identifier information of the SPS corresponding to the GPS.