Point Cloud Transmission with Segmented Geometry and Attribute Bitstreams
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Solution Overview
Problem
Existing technologies face challenges in efficiently transmitting and receiving point cloud data due to its large volume, requiring high throughput, and struggle with latency and encoding/decoding complexity.
Innovation Solution
A point cloud data transmission and reception system that includes a point cloud video acquirer, encoder, encapsulation module, transmitter, receiver, decapsulation module, decoder, and renderer, utilizing both geometry-based and video-based compression schemes to efficiently process and deliver point cloud content over networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high throughput to ensure data quality, then the quality of point cloud service is improved, but the latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is divided into multiple video sequences (geometry video sequence and attribute video sequences), which are then segmented into multiple independent bitstreams. This segmentation allows parallel processing and transmission, reducing overall latency while maintaining high throughput for quality service delivery.
Solution Approach 2:
The system dynamically selects between different compression schemes (geometry-based compression for first bitstreams, video-based compression for second bitstreams) based on the specific characteristics of the point cloud data. This dynamic adaptation optimizes the balance between transmission quality and processing latency for different data types.
2Reliability
If point cloud data is transmitted with high throughput to ensure data quality, then the quality of point cloud service is improved, but the encoding/decoding complexity increases
Solution Approach 1:
The encoding process is segmented into multiple independent encoding paths (geometry-based compression and video-based compression), allowing parallel processing that distributes computational complexity. The decoding side similarly processes multiple independent bitstreams, reducing the complexity burden on single encoding/decoding units while maintaining high overall quality.
Solution Approach 2:
The system changes the compression parameter approach by selecting different compression schemes (geometry-based vs. video-based) for different bitstreams based on data characteristics. This parameter variation allows optimization of encoding/decoding complexity for each specific data type while maintaining high throughput and service quality.
3Measurement precision
If the number of points in 3D space is large to represent accurate point cloud, then the precision of point cloud representation is improved, but the data volume and transmission requirements increase
Solution Approach 1:
The large set of 3D points is segmented into multiple video sequences (first video sequence for geometry, second video sequences for attributes). Each sequence is further divided into multiple bitstreams with different compression schemes. This hierarchical segmentation reduces the data volume of each individual stream while collectively representing the complete high-precision point cloud.
Solution Approach 2:
The system applies different compression parameters and schemes (geometry-based compression with one set of parameters, video-based compression with another set) to different portions of the point cloud data. This parameter differentiation allows efficient compression of large point clouds while maintaining the precision required for accurate 3D representation.
Data Source
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AI summary
Disclosed herein is a point cloud data transmission method including encoding the point cloud data, encapsulating the point cloud data, and transmitting point cloud data. Disclosed herein is a point cloud data reception device including a receiver configured to receive the point cloud data, a decapsulator configured to decapsulate the point cloud data, and a decoder configured to decode the point cloud data.