Point Cloud Bitstream Processing with Octree Difference Compression
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data due to latency and encoding/decoding complexity, which is crucial for applications such as virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and apparatus for efficiently processing point cloud data through encoding and decoding bitstreams containing point cloud data, utilizing geometry-based and video-based compression techniques, and incorporating feedback information to optimize data processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If geometry-based compression is used to reduce data size, then encoding/decoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple octrees at different levels. The encoder processes only the difference data between octrees at different levels rather than encoding the entire point cloud from scratch. This segmentation approach reduces the amount of data that needs to be encoded and decoded, thereby reducing encoding/decoding complexity while maintaining compression efficiency.
2Manufacturing precision
If high-quality point cloud processing is performed, then latency increases
Solution Approach 1:
The system performs preliminary actions by pre-processing point cloud data into octree structures and pre-calculating difference data between octree levels. This preparation work is done in advance so that when actual processing is needed, the system can quickly process only the essential difference data rather than processing the complete point cloud from scratch, thereby reducing latency while maintaining processing quality.
3Productivity
If feedback information is incorporated to optimize processing, then device complexity increases
Solution Approach 1:
The system incorporates feedback information about user interaction patterns and point cloud processing requirements. The feedback mechanism analyzes user behavior to dynamically adjust processing parameters such as octree level depth and difference data thresholds. This feedback-driven adaptation allows the system to optimize processing efficiency for each user's specific needs without requiring complex manual configuration, as the system automatically learns and adapts from user interaction feedback.
Data Source
AI summary
A point cloud data reception method according to embodiments can comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data. A point cloud data transmission method according to embodiments can comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data.