LiDAR is widely used to capture accurate 3D outdoor scene structures. However, LiDAR produces many undesirable noise points in snowy weather, which hamper analyzing meaningful 3D scene structures. Semantic segmentation with snow labels would be a straightforward solution for removing them, but it requires laborious point-wise annotation. To address this problem, we propose a novel self-supervised learning framework for snow points removal in LiDAR point clouds. Our method exploits the structural characteristic of the noise points: low spatial correlation with their neighbors. Our method consists of two deep neural networks: Point Reconstruction Network (PR-Net) reconstructs each point from its neighbors; Reconstruction Difficulty Network (RD-Net) predicts point-wise difficulty of the reconstruction by PR-Net, which we call reconstruction difficulty. With simple post-processing, our method effectively detects snow points without any label. Our method achieves the state-of-the-art performance among label-free approaches and is comparable to the fully-supervised method. Moreover, we demonstrate that our method can be exploited as a pretext task to improve label-efficiency of supervised training of de-snowing.

翻译：LiDAR 被广泛用来捕捉准确的 3D 户外场景结构。 但是, LiDAR 在雪天中产生许多不受欢迎的噪音点, 妨碍分析有意义的 3D 场景结构。 使用雪标签的语义分割是清除它们的一个直接的解决办法, 但需要用费力的点数说明。 为了解决这个问题, 我们提议了一个全新的自我监督的学习框架, 用于清除利DAR点云中的积雪点。 我们的方法利用了噪音点的结构特征: 与邻居的空间相关性低。 我们的方法由两个深层神经网络组成: 点重建网络(PR- Net) 从邻居那里重建每点; 重建困难网络(RD-Net) 预测了由我们称之为重建困难的PR- Net (RD- Net) 重建的点性困难。 在简单的后处理中, 我们的方法可以无任何标签地有效地探测积雪点。 我们的方法在无标签的方法中达到了最先进的表现, 并且与完全被监视的方法相仿。 此外, 我们证明, 我们的方法可以被利用作为借口, 提高监管的脱雪训练的标签效率。