Low-Latency Point Cloud Compression With G-PCC and V-PCC
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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-based point cloud compression (G-PCC) and video-based point cloud compression (V-PCC) coding, with feedback information used to optimize processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using traditional methods, then data representation is achieved, but processing latency is high and encoding/decoding complexity increases
Solution Approach 1:
The patent segments point cloud data into multiple tiles or patches, allowing parallel processing of different regions. This segmentation enables the system to process smaller data units simultaneously, reducing overall processing latency and improving throughput without compromising the完整性 of the point cloud representation.
Solution Approach 2:
The patent performs preliminary organization and preprocessing of point cloud data into structured formats (such as organized point clouds with defined grids or hierarchies) before encoding. This preliminary action optimizes the data structure for faster access and processing during decoding, reducing latency in real-time applications.
2Reliability
If detailed point cloud data is processed, then service quality is improved, but encoding/decoding complexity increases
Solution Approach 1:
The patent merges multiple processing stages and data representations into unified encoding and decoding frameworks. By combining geometry encoding, attribute encoding, and organizational structures into integrated workflows, the system maintains high service quality while reducing the overall complexity of the processing pipeline through consolidation rather than separate handling of each component.
Solution Approach 2:
The patent implements dynamic processing strategies where the level of detail and processing intensity are adjusted based on application requirements, available resources, and data characteristics. This dynamic approach allows the system to maintain high service quality when needed while reducing complexity in less demanding scenarios, providing adaptive performance optimization.
Data Source
AI summary
A method for processing point cloud data according to embodiments may comprise: encoding point cloud data; and transmitting the encoded point cloud data. The method for processing point cloud data according to embodiments may comprise: receiving point cloud data; and decoding the received point cloud data.