Scalable Point Cloud Transmission for Low-Latency 3D Compression
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for applications like virtual reality, augmented reality, and self-driving services due to latency and encoding/decoding complexity.
Innovation Solution
A method and device for encoding and decoding point cloud data using geometry-based and video-based compression techniques, including geometry-based point cloud compression (G-PCC) and video-based point cloud compression (V-PCC), with features like octree geometry coding, voxelization, and attribute encoding, to optimize data transmission and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If geometry-based point cloud compression (G-PCC) is used, then encoding precision is improved, but encoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple levels of detail (LOD), with coarse geometry encoded at lower levels and fine details encoded at higher levels. This segmentation allows the encoder to process and transmit data at different precision levels, reducing overall encoding complexity while maintaining high precision where needed.
Solution Approach 2:
The encoding precision is made dynamic and adaptive based on the complexity of the point cloud data being processed. The encoder automatically adjusts the level of detail and encoding precision for different regions or frames, optimizing the balance between encoding precision and computational complexity in real-time.
2Manufacturing precision
If high-resolution point cloud data is transmitted, then service quality is improved, but transmission latency increases
Solution Approach 1:
The high-resolution point cloud data is segmented into multiple layers of detail, allowing the receiver to reconstruct the scene at different quality levels. Lower-resolution versions can be transmitted and rendered faster, reducing latency, while higher-resolution details are transmitted separately for quality enhancement where needed.
Solution Approach 2:
Instead of transmitting all high-resolution data uniformly, the system transmits only the necessary level of detail for each region based on importance and visibility. This partial transmission approach maintains service quality for critical areas while reducing overall transmission latency.
3Manufacturing precision
If more point data is processed, then representation accuracy is improved, but processing time increases
Solution Approach 1:
The large set of point data is organized into hierarchical structures (such as octrees or point clusters) that enable processing at multiple levels of detail. This segmentation allows the system to process and render only the necessary number of points for the current view and quality requirement, improving processing efficiency while maintaining representation accuracy.
Solution Approach 2:
The system dynamically changes processing parameters such as point density, level of detail, and sampling rate based on the complexity of the scene and performance requirements. This allows accurate representation of complex geometries while reducing processing time for simpler areas.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data. In addition, a point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.