In this paper, we propose \emph{Neural Points}, a novel point cloud representation. Unlike traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuous geometric shape via neural fields. Therefore, Neural Points can express much more complex details and thus have a stronger representation ability. Neural Points is trained with high-resolution surface containing rich geometric details, such that the trained model has enough expression ability for various shapes. Specifically, we extract deep local features on the points and construct neural fields through the local isomorphism between the 2D parametric domain and the 3D local patch. In the final, local neural fields are integrated together to form the global surface. Experimental results show that Neural Points has powerful representation ability and demonstrate excellent robustness and generalization ability. With Neural Points, we can resample point cloud with arbitrary resolutions, and it outperforms state-of-the-art point cloud upsampling methods by a large margin.

翻译：在本文中, 我们提出 \ emph{ Neural Point}, 一个新的点云代表。 不同于传统的点云代表 3D 空间中每个点只代表位置或本地平面, 神经点的每个点代表着通过神经场的局部连续几何形状。 因此, 神经点可以表达更复杂的细节, 因而具有更强的代表能力。 神经点受过高分辨率表面的训练, 包含丰富的几何细节, 这样经过训练的模型有足够的表达能力来表达各种形状。 具体地说, 我们通过本地2D 参数域和 3D 本地补丁之间的异形态, 绘制点点的深点本地特征, 并构建神经场。 在最后, 本地神经场可以一起形成全球表面。 实验结果显示, 神经点具有强大的代表能力, 并展示出极强的稳健性和一般化能力。 在神经点中, 我们可以用任意的分辨率重新标定点云, 并且它以大边缘的状态点向上标的方法。