Abstract
Point cloud sampling is a less explored research topic for this data representation. The most common sampling methods nowadays are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, other than selecting points directly with mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which can capture the outline of input point clouds. Experimental results show that better performances are achieved with our sampling method due to the important outline information it learned.
Abstract (translated)
点云采样是这个数据表示领域中研究较少的话题之一。目前,最常见的采样方法仍然是传统的随机采样和最远的点采样。随着神经网络的发展,已经提出了多种方法以任务为基础学习样本点云。但是这些方法大多基于生成式方法,除了直接使用数学统计方法选择点。受到图像中卡内基边缘检测算法的启发,并结合注意力机制,本文提出了一种非生成式的注意力基于点云边缘采样方法(APES),可以捕捉输入点云的轮廓。实验结果表明,由于它学习了重要的轮廓信息,我们的采样方法取得了更好的性能。
URL
https://arxiv.org/abs/2302.14673
