Point Cloud Playback Mechanism for 6DoF Video
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Solution Overview
Problem
The rendering of point cloud data in six degree of freedom (6DoF) video is computationally expensive and requires large storage or transmission capacity due to its volumetric nature, making it challenging to deliver high frame rates and efficient storage solutions.
Innovation Solution
Prioritizing point cloud data based on the user's viewpoint and streaming higher priority data in real-time, while downloading lower priority data later, and optimizing point cloud data to enable view-dependent partitions for separate streaming, allowing seamless transitions between different viewport streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is rendered in real-time for 6DoF video, then immersive media experience is provided, but computational cost becomes excessively high
Solution Approach 1:
The point cloud data is divided into multiple priority levels (first priority, second priority, third priority) based on their importance for rendering. This segmentation allows the system to process and transmit only the most critical data first, reducing the immediate computational burden while maintaining visual quality.
Solution Approach 2:
Point cloud data is pre-encoded and organized into different priority streams before transmission. The encoder prioritizes certain points (e.g., those on visible surfaces or important objects) and prepares them for faster transmission and processing, enabling real-time playback without full computational processing of all points.
2Measurement precision
If all point cloud data is transmitted for complete scene representation, then rendering accuracy is improved, but transmission capacity requirements increase
Solution Approach 1:
Different regions or aspects of the point cloud data are assigned different quality levels based on their importance. Critical areas (such as foreground objects or areas near the viewpoint) are transmitted with higher precision, while less important areas use lower precision, optimizing the balance between rendering accuracy and data rate.
Solution Approach 2:
Instead of transmitting all point cloud data with equal priority, the system transmits only the necessary portion (partial action) at high priority for immediate rendering. Lower priority data is transmitted later or with reduced bandwidth, providing sufficient visual quality without requiring full data transmission capacity.
3Productivity
If point cloud data is optimized for view-dependent partitions, then streaming efficiency is improved, but data structure complexity increases
Solution Approach 1:
The point cloud data structure is organized dynamically based on the viewpoint. As the viewpoint changes, the priority assignments and partitioning of point cloud data adapt accordingly. This dynamic reorganization enables efficient streaming for different viewing angles without requiring complete re-encoding, improving streaming efficiency despite the added structural complexity.
Data Source
AI summary
An apparatus to facilitate real-time playback of point cloud sequence data is disclosed. The apparatus comprises one or more processors to receive point cloud data of a captured scene, decompose the point cloud data into a plurality of point cloud patches, wherein each point cloud patch is associated with an object in the scene and includes contextual information regarding the point cloud patch, encode each of the point cloud patches via a deep-learning based algorithm to generate encoded point cloud patches, receive a viewpoint selection from a client, assign a priority to data chunks within each encoded point cloud patch based on the viewpoint selection and the contextual information and transmit the data chunks to the client based on the assigned priority.