Point Cloud Data Encoding and Decoding Latency Reduction
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Solution Overview
Problem
Existing methods for processing point cloud data are inefficient, leading to latency and increased complexity in encoding and decoding, which hinders the delivery of high-quality point cloud services, especially in applications like virtual reality and self-driving services.
Innovation Solution
A method and device for efficiently processing point cloud data by encoding and transmitting bitstreams containing the point cloud data, utilizing techniques such as geometry-based and video-based point cloud compression, and employing devices capable of wired or wireless communication to manage the encoding, transmission, and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional point cloud processing methods are used, then processing capability is maintained, but latency increases and encoding/decoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple blocks or regions, and processing is performed on each segment independently and in parallel. This segmentation reduces the overall processing time and latency while distributing the encoding/decoding complexity across multiple smaller units, preventing any single processing unit from becoming a bottleneck.
Solution Approach 2:
Pre-processing operations such as point cloud registration, alignment, and initial filtering are performed before the main encoding/decoding process. By preparing the data in advance, the actual real-time processing requires less computational complexity and reduces latency during critical transmission and rendering phases.
2Productivity
If traditional point cloud processing methods are used, then processing capability is maintained, but processing efficiency decreases
Solution Approach 1:
The processing system dynamically adjusts the level of detail and processing intensity based on the specific characteristics of the point cloud data and real-time performance requirements. This dynamic adaptation optimizes processing efficiency by applying appropriate computational resources only where needed, rather than uniformly processing all data at maximum complexity.
Solution Approach 2:
Approximate representations or simplified copies of the point cloud data are generated for preliminary processing and visualization, while the full-resolution data is processed separately. This allows efficient preview and initial processing without requiring full computational complexity, improving overall processing efficiency while maintaining the capability to handle complete data when needed.
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. In addition, a point cloud data transmission device according to embodiments may comprise: an encoder which encodes point cloud data; and a transmitter which transmits a bitstream including the point cloud data. In addition, a point cloud data reception method may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data. In addition, a point cloud data reception device may comprise: a reception unit which receives a bitstream including point cloud data; and a decoder which decodes the point cloud data.