Point Cloud Compression for Low-Latency 3D Data Transmission
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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 like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for encoding and decoding point cloud data using geometry-based and video-based compression techniques, including geometry-based point cloud compression (G-PCC) and video-based point cloud compression (V-PCC), with processes like octree geometry coding, arithmetic encoding, and attribute transformation to optimize data transmission and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If point cloud data is represented using tens of thousands to hundreds of thousands of point data, then the quality and detail of the three-dimensional content is improved, but the data processing complexity and latency increase significantly
Solution Approach 1:
The patent divides the point cloud data into multiple layers or levels of detail, where coarse geometry is represented separately from fine details. This segmentation allows the system to process and transmit essential three-dimensional information efficiently while maintaining the option to add higher precision details only when needed, thereby reducing overall processing complexity while preserving representation quality.
Solution Approach 2:
The patent applies different levels of precision to different regions of the point cloud data. Critical areas requiring high accuracy (such as objects of interest or regions with important features) are represented with higher precision, while less important areas use lower precision representations. This local differentiation maintains manufacturing precision where needed while reducing overall data processing complexity.
2Reliability
If high-resolution point cloud data is transmitted for VR and self-driving services, then the service quality is improved, but the transmission latency and encoding/decoding complexity increase
Solution Approach 1:
The patent performs preliminary processing of point cloud data into a compressed hierarchical format before transmission. By pre-organizing the data structure and encoding it in advance at the source, the system reduces the encoding/decoding complexity and transmission time required at remote locations, thereby lowering overall latency while maintaining service quality for VR and self-driving applications.
Solution Approach 2:
The patent extracts and transmits only the essential geometric and attribute information needed for the service application, rather than transmitting complete high-resolution point cloud data. This selective extraction reduces data volume and transmission latency while maintaining sufficient service quality, as only the most critical information is transmitted and processed.
3Manufacturing precision
If comprehensive point cloud attributes (color, reflectance, transparency) are processed, then the visual quality and realism are improved, but the encoding and decoding complexity increase
Solution Approach 1:
The patent combines multiple attribute processing operations into unified encoding and decoding workflows. By merging geometric transformation, attribute mapping, and visual effect application into integrated processing stages, the system reduces overall encoding complexity while maintaining comprehensive visual quality including color, reflectance, and transparency properties.
Solution Approach 2:
The patent implements a universal processing framework that handles multiple attribute types (color, reflectance, transparency, etc.) through a single unified system. This multi-functional approach allows the same encoding and decoding infrastructure to process various attribute kinds simultaneously, reducing device complexity compared to having separate specialized processors for each attribute type.
Data Source
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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.