Point Cloud Processing Through Patch-Based Encoding and Decoding
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for virtual reality (VR), augmented reality (AR), mixed reality (MR), and self-driving services due to latency and encoding/decoding complexity.
Innovation Solution
A method and device for processing point cloud data by encoding geometry and attribute information, transmitting a bitstream, and decoding it efficiently using geometry and attribute information decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is processed using traditional methods, then the data can be represented accurately, but the processing latency and encoding/decoding complexity increase significantly
Solution Approach 1:
The patent segments point cloud data into multiple patches or tiles, processing each segment independently through encoding and decoding operations. This segmentation allows parallel processing of different regions, reducing overall processing latency while maintaining accurate representation of the complete point cloud data through systematic reconstruction of segmented portions
2Measurement precision
If point cloud data is processed using traditional methods, then the data can be represented accurately, but the encoding/decoding complexity increases significantly
Solution Approach 1:
The patent divides the encoding and decoding processes into separate, specialized modules that operate on segmented point cloud patches. This segmentation simplifies the complexity of processing entire point clouds by breaking down the operations into manageable, independent units that can be handled by dedicated encoding and decoding circuits, reducing overall device complexity while preserving data accuracy
Solution Approach 2:
The patent introduces intermediate data structures and processing stages between the raw point cloud input and final output, including patch-wise processing intermediates and coordinate system transformation layers. These intermediaries simplify the overall encoding/decoding complexity by creating structured intermediate representations that are easier to process while maintaining faithful representation of the original point cloud data
3Reliability
If large amounts of point data are used to represent point cloud content, then the quality of VR, AR, MR, and self-driving services improves, but the processing efficiency decreases
Solution Approach 1:
The patent segments large point cloud datasets into smaller patches for independent processing, enabling parallel computation across multiple processing units. This segmentation maintains service quality by ensuring each patch is processed with sufficient detail while improving processing efficiency through concurrent operations and reduced memory bandwidth requirements for individual processing units
Solution Approach 2:
The patent transforms the processing approach by adding spatial dimensionality through patch-based tiling and coordinate system transformations. This dimensional approach allows efficient memory access patterns and parallel processing across multiple spatial regions simultaneously, improving processing efficiency while maintaining the fidelity required for high-quality VR, AR, MR, and self-driving services
Data Source
AI summary
A method for transmitting point cloud data according to embodiments may encode and transmit point cloud data. A method for receiving point cloud data according to embodiments may receive and decode point cloud data.