Point Cloud Compression via Position-Based Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Point clouds used in autonomous driving vehicles require efficient compression and decompression methods to manage large data volumes effectively, especially for real-time communications and dynamic mapping applications, while maintaining quality metrics to ensure accurate object identification and tracking.
Innovation Solution
The proposed solution involves processing circuitry that determines weight factors for points in a point cloud based on their position and velocity vectors, allowing for quality metric calculation through weighted mean square error, which emphasizes the importance of points closer to the vehicle and adjusts based on vehicle speed, to optimize compression and decompression processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed to reduce data volume, then storage and transmission efficiency is improved, but quality metric and accuracy of object identification deteriorates
Solution Approach 1:
The patent applies local quality by assigning different weight factors to different points in the point cloud based on their spatial position and velocity characteristics. Points closer to the vehicle or with higher velocity are assigned higher weights, ensuring that compression preserves more detail in these important regions while allowing greater compression in less critical areas. This resolves the contradiction by maintaining measurement precision locally where it matters most while achieving overall data volume reduction.
2Ease of operation
If all points in the point cloud are processed with equal compression quality, then simplicity of processing is maintained, but efficiency of real-time communication deteriorates
Solution Approach 1:
The patent segments the point cloud processing into different categories based on position and velocity thresholds. Points are divided into groups (e.g., close/far points, high/low velocity points) that receive different compression treatments. This segmentation enables differentiated compression strategies that improve real-time communication efficiency by focusing resources on important points while simplifying processing of less critical points, thus resolving the contradiction between processing simplicity and communication efficiency.
3Speed
If compression is applied to reduce transmission time, then speed of data transmission is improved, but loss of information increases
Solution Approach 1:
The patent changes the parameter of compression intensity dynamically based on point characteristics (position and velocity). Instead of uniform compression, the system adjusts compression parameters locally - applying stronger compression to points with lower importance (e.g., distant, low-velocity points) and weaker compression to points with higher importance. This resolves the contradiction by enabling faster transmission through compression while minimizing information loss in critical regions through adaptive parameter adjustment.
Data Source
AI summary
Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable medium for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. The processing circuitry determines, for each point in a first point cloud, a corresponding point in a second point cloud. The first point cloud is a processed point cloud based on the second point cloud. The processing circuitry determines, for each point in the first point cloud, a corresponding error vector based on the corresponding point in the second point cloud. The processing circuitry determines, for each point in the first point cloud, a corresponding weight factor based on a position of the respective point in the first point cloud. The processing circuitry determines a quality metric for the first point cloud based on the error vectors and the weight factors.