Point Cloud Data Processing Device Using Patch Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for processing point cloud data face challenges in efficiently handling large datasets, leading to latency and encoding/decoding complexity, particularly in applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for encoding and decoding point cloud data, including geometry and attributes such as color or reflectance, are developed to efficiently process and transmit point cloud data, utilizing techniques like geometry-based and video-based point cloud compression coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using traditional methods, then the data can be represented accurately, but the processing efficiency is low and latency is high
Solution Approach 1:
The patent segments point cloud data into multiple patches or tiles, allowing parallel processing of different regions. This segmentation enables the system to process large point cloud datasets more efficiently by dividing the workload across multiple processing units, thereby improving productivity while reducing overall processing latency.
Solution Approach 2:
The patent performs preliminary actions by pre-processing point cloud data during encoding, including geometry compression and attribute encoding. This preliminary processing reduces the computational burden during real-time decoding and rendering operations, improving processing efficiency and reducing latency in applications like VR and autonomous driving.
2Manufacturing precision
If detailed point cloud data is processed, then high quality is achieved, but encoding and decoding complexity increases
Solution Approach 1:
The patent applies parameter changes by using different compression rates and quality levels for geometry and attributes. It employs advanced encoding parameters such as octree depth control, attribute compression settings, and patch-based processing parameters that allow the system to maintain high data quality while managing encoding and decoding complexity through optimized parameter selection.
Solution Approach 2:
The patent extracts and processes only the essential features and attributes needed for the specific application. By selectively encoding geometry information and relevant attributes (such as color, reflectance) while omitting redundant data, the system achieves high quality point cloud representation with reduced encoding and decoding complexity.
Data Source
AI summary
A point cloud data processing method, according to embodiments, enables encoding and transmitting point cloud data. A point cloud data processing method, according to embodiments, enables receiving and decoding point cloud data.