Point Cloud Transmission With Segmented Encoding and Rendering
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Solution Overview
Problem
The generation and transmission of point cloud data is challenging due to its large number of points and the high throughput required, which complicates encoding and decoding processes and leads to latency issues.
Innovation Solution
A method and apparatus for efficiently transmitting and receiving point cloud data through encoding, decoding, and rendering processes, utilizing geometry-based and video-based point cloud compression techniques, along with feedback mechanisms to optimize data processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high throughput to maintain data quality, then the quality of point cloud service is improved, but the encoding and decoding complexity increases and latency issues arise
Solution Approach 1:
The patent segments point cloud data into multiple blocks or regions for independent processing. Each segment can be encoded and decoded separately, reducing the overall complexity while maintaining data quality. This segmentation allows parallel processing and optimizes the throughput-quality tradeoff by prioritizing critical segments.
Solution Approach 2:
The patent extracts and transmits only the most essential point cloud data features or attributes that contribute most to service quality. By selecting and transmitting only critical data elements, the system maintains high service quality while reducing the total data volume and processing complexity.
2Reliability
If point cloud data is transmitted with high throughput to maintain data quality, then the quality of point cloud service is improved, but latency increases
Solution Approach 1:
By dividing point cloud data into segments, the system can transmit and process smaller units in parallel, reducing overall latency. Critical segments can be prioritized for faster transmission while less critical segments follow, maintaining quality without uniform latency across all data.
Solution Approach 2:
The patent performs preliminary processing, filtering, or prioritization of point cloud data before transmission. Essential data is prepared and queued for immediate transmission, while less critical data is processed afterward, reducing latency for time-sensitive operations while maintaining overall data quality.
3Device complexity
If the number of points in 3D space is reduced to simplify data generation, then encoding and decoding complexity is reduced, but the quality of point cloud service deteriorates
Solution Approach 1:
The patent extracts and retains only the most significant point cloud points that contribute to service quality. By identifying and keeping critical points while discarding redundant ones, the system reduces data complexity while preserving the essential features needed for high-quality service delivery.
Solution Approach 2:
The patent applies different quality levels to different regions or types of point cloud data. Critical regions maintain high point density and quality, while less important regions use lower density. This local differentiation reduces overall complexity while maintaining service quality where it matters most.
Data Source
AI summary
A point cloud data transmitting method according to embodiments can comprise the steps of encoding point cloud data and transmitting a bitstream including the point cloud data. A point cloud data receiving method according to embodiments can comprise the steps of receiving a bitstream including point cloud data, decoding the point cloud data, and rendering the point cloud data.