3D Point Cloud Encoding Merging Datasets
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Solution Overview
Problem
Current three-dimensional data encoding methods lack efficiency in compressing and transmitting point cloud data, which is essential for applications like autonomous vehicles and infrastructure inspection, due to the large amount of data involved and the absence of effective methods for multiplexing and decoding with multiple codecs.
Innovation Solution
A method that combines first and second point cloud data, encoding geometry information and identification information as attribute information to improve coding efficiency, allowing for better compression and transmission of three-dimensional data by using a processor to generate encoded data and decode it effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is encoded separately without combining multiple datasets, then the encoding process is simpler, but the coding efficiency and compression ratio are reduced
Solution Approach 1:
The patent combines multiple point cloud datasets (first point cloud data and second point cloud data) into a single third point cloud data structure before encoding. This merging approach allows the encoder to process and compress data from multiple sources simultaneously, improving coding efficiency and compression ratio while maintaining a unified encoding process that does not significantly increase complexity
2Productivity
If identification information is encoded as attribute information, then coding efficiency is improved through better compression, but the decoding process becomes more complex
Solution Approach 1:
The patent merges identification information with attribute information by encoding the identification information as part of the attribute information structure. This allows the encoder to compress both types of information together using the same encoding mechanisms, improving overall coding efficiency. The decoder can separately extract identification information from the combined attribute information without requiring a fundamentally different decoding process
3Quantity of substance
If multiple point cloud datasets are processed independently, then the processing is more flexible, but the compression ratio and data transmission efficiency are reduced
Solution Approach 1:
The patent combines multiple point cloud datasets into a unified third point cloud data structure that maintains the distinction between original datasets through identification information. This merging enables the encoder to apply compression algorithms more effectively across the combined data, improving compression ratio and data transmission efficiency while preserving the ability to reference and process individual original datasets when needed
Data Source
AI summary
A three-dimensional data encoding method includes: obtaining third point cloud data that is a combination of first point cloud data and second point cloud data, and includes geometry information of each of three-dimensional points included in the third point cloud data, and identification information indicating which of the first point cloud data and the second point cloud data each of the three-dimensional points belongs to; and generating encoded data by encoding the third point cloud data obtained. In the generating, identification information of each of the three-dimensional points is encoded as attribute information.