Point Cloud Encoding With Attribute-Origin Signaling for Lower Decode Load
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Solution Overview
Problem
Existing methods for encoding and decoding three-dimensional data, particularly point cloud data, result in high processing demands, lacking efficient methods for combining and decoding data from multiple codecs and supporting system formats.
Innovation Solution
A method and device for encoding three-dimensional data by combining first and second point cloud data, generating encoded data with geometry and attribute information, and using control information to determine data origin, reducing processing load through lossless encoding and motion adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using existing encoding methods, then data transmission and storage efficiency is improved, but processing complexity and computational load increase significantly
Solution Approach 1:
The point cloud data is divided into multiple layers including base layer and enhancement layers. Each layer contains point cloud data with different levels of detail, allowing progressive decoding where lower layers provide coarse structure and higher layers add fine details, reducing overall processing complexity while maintaining compression efficiency
Solution Approach 2:
Motion adjustment information is encoded in advance to pre-align point cloud data from different views or time points before combining them. This preliminary alignment reduces the computational burden during decoding by avoiding complex real-time registration operations
2Productivity
If multiple point cloud data sets are combined and encoded together, then coding efficiency is improved, but the complexity of managing and processing the combined data increases
Solution Approach 1:
The patent introduces a temporal or hierarchical dimension by organizing combined point cloud data into sequential layers or groups. Instead of treating all data points uniformly, the encoding process operates on structured layers with clear boundaries and dependencies, making management and processing more systematic and efficient
3Measurement precision
If detailed attribute information is stored for each three-dimensional point, then data accuracy and quality are improved, but data volume and processing requirements increase
Solution Approach 1:
Different regions or layers of the point cloud data are assigned different levels of attribute information detail. Critical regions requiring high precision (such as foreground objects or areas of interest) retain full attribute information, while less critical regions use compressed or simplified representations, optimizing the balance between accuracy and data volume
Data Source
AI summary
A three-dimensional data encoding method includes: generating encoded data by encoding third point cloud data that is a combination of first point cloud data and second point cloud data; and generating a bitstream including the encoded data and control information. The encoded data includes a piece of geometry information and pieces of attribute information of each of three-dimensional points included in the third point cloud data. One of the pieces of attribute information includes first information indicating whether a corresponding one of the three-dimensional points belongs to the first point cloud data or the second point cloud data. The control information includes second information indicating, among the pieces of attribute information, a piece of attribute information including the first information.