Point Cloud Compression With Feedback-Guided Low-Latency Decoding
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for applications like virtual reality, augmented reality, and self-driving services due to latency and encoding/decoding complexity.
Innovation Solution
A method and device for encoding and decoding point cloud data using geometry-based and video-based compression techniques, along with feedback information to optimize data processing based on user interaction, reducing latency and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometry-based compression is used to reduce data size, then encoding/decoding complexity increases, but processing efficiency improves
Solution Approach 1:
The patent segments point cloud data into multiple octree levels and divides the octree structure into multiple trees. This segmentation allows the system to process only relevant portions of the point cloud, reducing the overall encoding/decoding complexity while maintaining processing efficiency through selective decoding of occupied nodes.
Solution Approach 2:
The patent implements dynamic decoding where the decoder can adaptively select which octree levels and nodes to decode based on feedback information. This dynamic approach allows the system to adjust the decoding complexity in real-time, balancing processing efficiency with computational resources required.
2Loss of time
If feedback-based optimization is implemented to reduce latency, then device complexity increases, but response time improves
Solution Approach 1:
The patent implements a feedback mechanism where the decoder sends feedback information to the encoder about decoding status and requirements. This feedback loop enables the encoder to adjust encoding parameters and prioritize data transmission, significantly reducing latency by ensuring only necessary data is transmitted and processed.
Solution Approach 2:
The system performs preliminary actions by pre-processing point cloud data into octree structures and preparing encoding parameters in advance. This preliminary preparation, combined with feedback-based optimization, allows the system to respond more quickly to changing requirements without increasing overall system complexity.
3Productivity
If point cloud data is processed in real-time for VR applications, then processing efficiency improves, but encoding/decoding complexity increases
Solution Approach 1:
The patent segments point cloud data into multiple octree levels and divides the octree structure into multiple trees. This segmentation allows the system to process only relevant portions of the point cloud in real-time, reducing the overall encoding/decoding complexity while maintaining processing efficiency through selective decoding of occupied nodes.
Solution Approach 2:
The patent implements dynamic decoding where the decoder can adaptively select which octree levels and nodes to decode based on feedback information. This dynamic approach allows the system to adjust the decoding complexity in real-time, balancing processing efficiency with computational resources required.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise 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 may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.