Point Cloud Data Transmission Device Encoding Latency
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Solution Overview
Problem
Existing methods for processing point cloud data face challenges in efficiency and complexity, particularly in terms of latency and encoding/decoding processes, which hinder high-quality service delivery in applications like virtual reality and self-driving services.
Innovation Solution
A method and device for efficiently processing point cloud data through encoding and transmitting a bitstream containing the data, utilizing techniques such as geometry-based and video-based point cloud compression coding, and decoding this bitstream to reconstruct high-quality point cloud content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using traditional methods, then the processing can be performed, but the latency is high and encoding/decoding complexity increases
Solution Approach 1:
The patent segments the point cloud data processing into multiple independent modules including geometry decoding, attribute decoding, and prediction modules. Each module processes specific aspects of the data independently, allowing for optimized processing paths and reduced overall latency while maintaining processing efficiency.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing prediction information (such as neighboring point attributes and geometry data) in buffers before actual decoding occurs. This preparation work reduces the computational burden during real-time processing, thereby reducing latency without compromising processing efficiency.
2Productivity
If point cloud data is processed using traditional methods, then the processing can be performed, but the encoding/decoding complexity increases
Solution Approach 1:
The patent divides the complex encoding/decoding process into separate geometry decoding and attribute decoding stages, with each stage having dedicated simplified modules. This segmentation reduces the complexity of individual modules while maintaining overall processing efficiency through coordinated operation of the segmented components.
Solution Approach 2:
The patent introduces intermediary buffers and prediction modules that mediate between the geometry and attribute decoding processes. These intermediaries store and transfer processed information in optimized formats, reducing the computational complexity of the main decoding operations while preserving processing efficiency.
Data Source
AI summary
A point cloud data transmission method according to the embodiments may comprise the steps of encoding point cloud data, and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to the embodiments may comprise the steps of receiving a bitstream comprising point cloud data, and decoding the point cloud data.