3D Point Cloud Attribute Encoding via Sub-block Segmentation
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Solution Overview
Problem
Current methods for encoding and decoding three-dimensional data, such as point cloud data, are limited in processing attribute information that exceeds the length compliant with point cloud compression standards, leading to inefficiencies in data transmission and processing.
Innovation Solution
A three-dimensional data decoding and encoding method that involves obtaining control information to merge attribute information into sub-blocks of predetermined lengths, allowing for the processing and encoding of attribute information exceeding the standard length, using techniques like transforming and partitioning to reduce data size and improve encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If attribute information is encoded using standard point cloud compression methods, then compatibility with compression standards is maintained, but attribute information exceeding standard length cannot be processed
Solution Approach 1:
The attribute information is divided into multiple sub-blocks, each with a predetermined length that complies with the point cloud compression standard. This segmentation allows the system to process attribute information exceeding standard length while maintaining compatibility, as each sub-block can be encoded using standard methods and the complete attribute information is reconstructed by combining the sub-blocks.
2Reliability
If attribute information is divided into sub-blocks of predetermined length, then compatibility with compression standards is maintained, but the complexity of encoding and decoding processes increases
Solution Approach 1:
The attribute information is divided into sub-blocks before encoding, and the division information is recorded in advance. During decoding, the recorded division information is used to reconstruct the complete attribute information by combining the decoded sub-blocks. This preliminary action simplifies the encoding and decoding processes by pre-establishing the structure, avoiding the need for complex real-time processing.
3Productivity
If attribute information is processed as a single large block, then processing simplicity is maintained, but encoding efficiency decreases for large datasets
Solution Approach 1:
The attribute information is segmented into multiple sub-blocks of predetermined length, which improves encoding efficiency for large datasets. Each sub-block can be encoded independently using standard point cloud compression methods, allowing parallel processing and reducing the overall encoding time. The segmentation structure, combined with the recorded division information, maintains manageable processing complexity while significantly enhancing productivity.
Data Source
AI summary
A three-dimensional data decoding method includes: obtaining control information indicating that first attribute information of a three-dimensional point and second attribute information of the three-dimensional point are to be merged; and decoding the first attribute information and the second attribute information according to the control information. The first attribute information and the second attribute information each have a predetermined length compliant with a point cloud compression standard.