3D Point Cloud Encoding With Shared Frame Region Information
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Solution Overview
Problem
Existing three-dimensional data processing methods face challenges in reducing the amount of data to be processed, particularly in the context of point cloud compression, which is essential for efficient transmission and storage.
Innovation Solution
A method and device for encoding and decoding three-dimensional data that involves generating common and frame-specific information to reduce redundant data processing, including region information and partial point cloud features, enabling efficient encoding and decoding of point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed for transmission and storage, then the amount of data is reduced, but the processing complexity increases due to the need for encoding and decoding operations
Solution Approach 1:
The point cloud data is divided into multiple frames, and each frame is further segmented into prediction blocks and residual blocks. This segmentation allows for differential processing where prediction blocks use reference frame information and residual blocks contain only the differences, reducing overall data量 while maintaining processing efficiency through structured organization.
Solution Approach 2:
Reference frames are decoded and stored in advance before processing current frames. Prediction blocks are generated using preliminary reference information from previously decoded frames, which reduces the processing load on current frame data and enables more efficient compression by leveraging temporal redundancy.
2Measurement precision
If all point cloud data is processed and stored in full detail, then the processing precision is maintained, but the storage requirements and network bandwidth consumption increase significantly
Solution Approach 1:
The invention extracts and separates prediction information from residual information. Prediction blocks contain the majority of the point cloud data that can be inferred from reference frames, while residual blocks contain only the essential differences. This extraction allows storage of only the critical residual data at high precision while prediction data is reconstructed from reference frames.
Solution Approach 2:
Different processing precision is applied to different parts of the data. Prediction blocks use reference frame information with standard precision, while residual blocks contain the critical difference information with higher precision to maintain overall accuracy. This local differentiation optimizes the balance between storage efficiency and processing precision.
3Loss of energy
If redundant information is eliminated to reduce data size, then the network bandwidth efficiency is improved, but the information completeness may be compromised
Solution Approach 1:
Decoded reference frames are stored and fed back as input for decoding subsequent frames. This feedback mechanism allows the system to reuse previously decoded information for predicting current frame data, eliminating redundant transmission of identical or similar information while maintaining complete reconstruction capability through the residual blocks that capture all necessary differences.
Data Source
AI summary
A three-dimensional data encoding method includes: generating one or more items of first information each of which is common to two or more frames among a plurality of frames and includes information indicating one or more partial regions included in a frame; generating items of second information each of which is for a different one of the plurality of frames and includes information indicating a feature of a partial point cloud consisting of three-dimensional points of each of the partial regions; and encoding, for each item of data of the partial point cloud, point cloud data of the three-dimensional points. The items of second information include information indicating one of the one or more items of first information.