Point Cloud Reception Using LoD Subgroups and Neighbor Search
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Solution Overview
Problem
Existing methods for processing point cloud data are inefficient due to high computational complexity and latency, requiring improved methods for encoding, decoding, and scalable services.
Innovation Solution
A method and device for receiving and rendering point cloud data by decoding geometry and attribute data, generating Level of Detail (LoD) for subgroups, and performing neighbor searches to derive attribute information efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted and received with encoding for compression and decoding for decompression, then data transmission quality is improved, but computational operation becomes complicated and time-consuming due to the large volume of point cloud data
Solution Approach 1:
The point cloud data is divided into multiple subgroups based on spatial relationships and depth information. Each subgroup is processed independently through encoding and decoding operations, which reduces the computational burden on any single processing unit while maintaining overall data quality. The segmentation allows parallel processing and reduces the complexity of handling large-volume point cloud data as a whole.
2Reliability
If point cloud data is transmitted and received with encoding for compression and decoding for decompression, then data transmission quality is improved, but processing time increases due to the large volume of point cloud data
Solution Approach 1:
Depth information is extracted and preprocessing operations are performed on point cloud data before full encoding and decoding. Neighbor relationships are pre-identified and subgroup structures are established in advance, which reduces the computational work required during actual transmission and decoding phases, thereby reducing overall processing time while maintaining data quality.
3Reliability
If traditional point cloud processing methods are used, then complete point cloud data can be processed, but latency increases and encoding/decoding complexity increases
Solution Approach 1:
Depth information is extracted from the point cloud data as a separate component. This extracted depth information is used to create subgroup structures and identify neighbor relationships, allowing the main point cloud processing to proceed more efficiently. By separating and utilizing depth information independently, the system reduces latency and encoding/decoding complexity while still processing complete point cloud data.
4Device complexity
If point cloud data is processed without subgroup division, then processing is simpler, but scalable services and efficiency are reduced
Solution Approach 1:
Point cloud data is divided into subgroups based on spatial coordinates and depth information, enabling parallel and distributed processing. This segmentation improves service efficiency and scalability by allowing different subgroups to be processed simultaneously across multiple processing units, while the grouping strategy maintains reasonable processing complexity through organized data structures.
Data Source
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AI summary
A point cloud data reception method and device are disclosed. The point cloud data reception method according to embodiments may comprise the steps of: receiving geometry data, attribute data, and signaling information; decoding the geometry data on the basis of the signaling information; decoding the attribute data on the basis of the signaling information and the decoded geometry data; and rendering point cloud data including the decoded geometry data and the decoded attribute data.