The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

规则域的不规则和缺乏排序使设计用于点云处理的深度神经网络具有挑战性。本文提出了一个名为Point Cloud Transformer的新颖框架（PCT）用于点云学习。PCT基于Transformer，在自然语言处理方面取得了巨大的成功，并在图像处理方面显示出巨大的潜力。它本质上是置换不变的，可以处理一系列点，使其非常适合点云学习。为了更好地捕获点云中的本地上下文，我们在最远点采样和最近邻居搜索的支持下增强了输入嵌入。大量的实验表明，PCT在形状分类，零件分割，语义分割和常规估算任务方面达到了最先进的性能。