Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation

Point cloud is a discrete and unordered expression of 3D data. A lot of methods have been proposed to solve the problem in 3D object classification and scene recognition. To handle the huge amount of unordered point cloud, down-sampling before processing is needed. The shortage of existing sampling methods is the lack of geometry information consideration, which is essential for point cloud classification and segmentation tasks. Our method is mainly motivated by the observation that points with a high curvature variation can depict the outlines of objects. Thus, we propose a curvature variation based sampling method for point cloud classification and segmentation tasks. We aim to sample points with high curvature variations, which are considered to be more suitable for classification and segmentation tasks than the traditional sampling method. We combine the proposed sampling algorithm with the existing sampling method for multiple information fusion, and a higher accuracy and mean IoU can be achieved. The experimental results verify the advantage of considering curvature variation in classification and segmentation tasks.

Automated summary

The research proposes a curvature variation based sampling method for point cloud classification and segmentation, which is motivated by the observation that points with high curvature variation can depict object outlines effectively. By combining this method with existing sampling techniques, a higher accuracy and mean IoU can be achieved, demonstrating the advantage of considering curvature variation in classification and segmentation tasks.