Point Cloud Feature Cubes With Attention for Efficient Detection
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Solution Overview
Problem
Processing large quantities of point cloud data for target detection consumes excessive computer resources and lacks focus on important information, leading to inefficiency and reduced accuracy in prediction results.
Innovation Solution
The method involves dividing point cloud data into target cubes and using an attention mechanism to update features of non-empty cubes, employing local and global attention operators to enhance feature extraction, while maintaining edge features and reducing unnecessary processing of empty cubes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all point clouds are sampled to determine target key points, then target detection accuracy is improved, but computer resource consumption increases significantly
Solution Approach 1:
The point cloud data is divided into multiple cubes (spatial segments), and only non-empty cubes containing actual points are processed further. This segmentation allows the system to avoid sampling and processing empty regions, significantly reducing computational resource consumption while maintaining detection accuracy in relevant areas.
Solution Approach 2:
The patent applies different processing strategies to different spatial regions: non-empty cubes receive detailed sampling and feature extraction, while empty cubes are quickly identified and skipped. This local quality approach ensures high accuracy where needed (in regions with objects) while minimizing resource usage in empty regions.
2Loss of information
If all point clouds are processed uniformly, then comprehensive feature extraction is achieved, but processing efficiency decreases
Solution Approach 1:
By segmenting point clouds into cubes and processing only non-empty ones, the patent avoids unnecessary processing of empty regions while ensuring all relevant points are captured. This maintains feature extraction completeness for actual objects while improving processing efficiency through selective handling.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of point clouds (non-empty cubes) rather than all cubes uniformly. This selective processing maintains sufficient feature extraction for detection accuracy while eliminating wasteful processing of empty regions, thereby improving overall efficiency.
Data Source
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AI summary
A point cloud data processing method, a neural network training method, and a related device are provided. The method may be applied to the field of point cloud data processing in the field of artificial intelligence. The method may include: obtaining point cloud data corresponding to a target environment, where the point cloud data is divided into a plurality of target cubes; generating an initial feature of each target cube based on initial information of a target point in each target cube; updating initial features of the plurality of target cubes based on an attention mechanism to obtain updated features of the plurality of target cubes; and performing a feature processing operation on the updated features of the plurality of target cubes to obtain a prediction result corresponding to the point cloud data. This reduces computer resources consumed in a processing process of the entire point cloud data, and helps focus on important information carried in the point cloud data to improve accuracy of the output prediction result.