Point Cloud Data Transmission Using V-PCC Component Segmentation
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Solution Overview
Problem
The efficient transmission and reception of point cloud data is hindered by high latency and encoding/decoding complexity, particularly in applications like virtual reality, augmented reality, and self-driving services, due to the large number of points in three-dimensional spaces and the resulting high throughput requirements.
Innovation Solution
A method and apparatus for point cloud data transmission and reception that involves acquiring, encoding, and transmitting point cloud video data using a video-based point cloud compression (V-PCC) process, which divides the data into geometry, attribute, and occupancy map components, and includes a file/segment encapsulation and decoding process to efficiently deliver and render point cloud content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is transmitted with high fidelity to maintain good quality services, then the quality of point cloud service is improved, but the data volume and transmission bandwidth requirements increase significantly
Solution Approach 1:
The patent segments point cloud data into multiple patches and divides the attribute data into multiple components (diffuse reflection, specular reflection, transmission, absorption). This segmentation allows selective transmission of only necessary components based on viewing conditions, reducing overall data volume while maintaining quality where needed.
Solution Approach 2:
The patent applies different transmission strategies to different regions and components based on their importance and visibility. Critical components like diffuse reflection are transmitted with higher fidelity, while less critical components use lower precision representations, achieving quality where it matters most with reduced overall data volume.
2Adaptability or versatility
If comprehensive point cloud data including multiple attributes is transmitted to support various services, then the versatility of point cloud services is improved, but the encoding complexity and processing requirements increase
Solution Approach 1:
The patent separates point cloud attribute data into distinct components (diffuse, specular, transmission, absorption) that can be independently encoded and transmitted. This allows receivers to selectively decode only the components needed for their specific service application, reducing encoding complexity while maintaining service versatility.
Solution Approach 2:
The patent creates a universal encoding framework that supports multiple services (VR, AR, self-driving) through a single multi-component data structure. The same encoded bitstream can be used across different applications, each selecting the appropriate components for their needs, thereby achieving versatility without proportionally increasing complexity.
3Speed
If real-time point cloud transmission is implemented for applications like self-driving, then the response speed is improved, but the latency increases due to processing requirements
Solution Approach 1:
The patent performs encoding and component separation in advance during the capture phase, creating pre-processed bitstreams that can be rapidly transmitted and decoded. This preliminary processing reduces the computational burden during real-time operation, minimizing latency while maintaining response speed.
Solution Approach 2:
The patent extracts and transmits only the essential components needed for real-time applications, separating critical data from optional details. This extraction allows receivers to process only necessary information quickly, reducing latency while maintaining adequate response speed for time-sensitive applications.
Data Source
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AI summary
Abstract: A point cloud data transmission method according to embodiments can include the steps of encoding point cloud data, and transmitting a bitstream including the point cloud data. A point cloud data reception method according to embodiments can include the steps of receiving point cloud data, decoding the point cloud data, and rendering the point cloud data.