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.

中文翻译：

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用于点云分类和分割的曲率变化启发式采样

点云是 3D 数据的离散和无序表达。已经提出了很多方法来解决3D对象分类和场景识别中的问题。为了处理大量无序点云，需要在处理前进行下采样。现有采样方法的不足之处在于缺乏几何信息考虑，这对于点云分类和分割任务至关重要。我们的方法的主要动机是观察到具有高曲率变化的点可以描绘物体的轮廓。因此，我们提出了一种基于曲率变化的点云分类和分割任务的采样方法。我们的目标是采样具有高曲率变化的点，这被认为比传统的采样方法更适合分类和分割任务。我们将提出的采样算法与现有的多信息融合采样方法相结合，可以获得更高的准确率和平均 IoU。实验结果验证了在分类和分割任务中考虑曲率变化的优势。