Adaptive Mode Selection for 3D Point Cloud Compression
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Solution Overview
Problem
Conventional point cloud compression techniques are computationally expensive and require high memory resources due to the need for multiple encoding and decoding operations to compress 3D point clouds.
Innovation Solution
The proposed solution involves an electronic device that uses adaptive mode selection for point cloud compression, where the device partitions the 3D point cloud into 3D blocks and determines mode decision information to select optimal encoding modes, reducing the number of encoding and decoding operations required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud compression techniques are used, then compression is achieved, but computational complexity and memory requirements increase significantly
Solution Approach 1:
The point cloud data is divided into multiple 3D blocks or partitions, allowing independent processing of each segment. This segmentation enables parallel computation and reduces the memory footprint required for processing the entire point cloud simultaneously, thereby improving compression efficiency while managing computational complexity.
Solution Approach 2:
The method performs preliminary classification of 3D points into different categories (e.g., interior, exterior, boundary points) before the main compression process. This preliminary action allows the subsequent compression algorithms to operate more efficiently on already-categorized data, reducing the computational burden during the actual compression phase.
2Manufacturing precision
If multiple encoding and decoding operations are performed, then compression quality is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies different levels of compression operations to different regions of the point cloud based on their importance. Critical regions receive more rigorous encoding/decoding operations to maintain high quality, while less critical regions undergo simpler processing. This partial application of full compression operations reduces overall processing time while maintaining acceptable compression quality.
Solution Approach 2:
Different compression strategies and quality levels are applied to different spatial regions of the point cloud based on local characteristics. Areas with high geometric significance or dense point distributions receive more processing attention, while sparse or less important regions use faster, simpler compression methods, optimizing the trade-off between quality and processing time.
Data Source
AI summary
An electronic device and method for adaptive mode selection for point cloud compression, is provided. The electronic device receives a 3D point cloud geometry and partitions the 3D point cloud geometry into a set of 3D blocks. For a 3D block of the set of 3D blocks, mode decision information is determined. The mode decision information includes class information of the 3D point cloud geometry, operational conditions associated with an encoding stage of the 3D point cloud geometry, or mode-related information associated with one or more 3D blocks of the set of 3D blocks. Based on the mode decision information, one or more modes are selected for the 3D block from a plurality of modes. Each mode corresponds to a function that is used to encode the 3D block. The 3D block is encoded based on the one or more modes.