Point Cloud Encoding Using Level of Detail Grouping
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Solution Overview
Problem
Existing methods for processing point cloud data face challenges in efficiently handling large amounts of data, leading to latency and complexity in encoding and decoding processes.
Innovation Solution
A method and device for processing point cloud data that involves encoding geometry and attribute data, grouping points into levels of detail (LOD), predicting geometry data using a prediction tree, and transmitting a bitstream containing the encoded data and signaling information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is processed using existing methods, then the data can be represented in three-dimensional space, but the encoding and decoding complexity increases significantly due to the large amount of point data
Solution Approach 1:
The point cloud data is divided into multiple levels of detail (LOD), where each level contains a subset of points. This segmentation allows the system to process and transmit only the necessary amount of data at each resolution level, reducing overall encoding and decoding complexity while maintaining accurate 3D representation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the point cloud data by organizing points across multiple levels of detail. This additional organizational dimension allows efficient management of the large volume of point data, reducing processing complexity without sacrificing spatial representation accuracy.
2Reliability
If all point data is transmitted and processed, then complete point cloud content is achieved, but latency increases due to the large data volume
Solution Approach 1:
The system transmits and processes only a partial subset of point data at each level of detail, rather than all points simultaneously. This partial action approach reduces the data volume requiring immediate processing, thereby reducing latency while still achieving complete point cloud content through progressive refinement across multiple levels.
3Reliability
If high-quality point cloud services are provided for VR and self-driving applications, then service quality improves, but the amount of data to be processed increases
Solution Approach 1:
Different levels of detail are assigned to different regions or aspects of the point cloud data based on their importance for specific applications. For VR and self-driving services, critical regions receive higher detail levels while less critical areas use lower detail levels, maintaining service quality while reducing overall data volume.
Data Source
AI summary
A method for transmitting point cloud data according to embodiments may comprise the steps of: encoding the point cloud data; and/or transmitting a bitstream including the point cloud data and signaling information. A method for receiving point cloud data according to embodiments may comprise the steps of: receiving a bitstream including the point cloud data and signaling information; decoding the point cloud data; and/or rendering the point cloud data.