The Devils in the Point Clouds: 研究点云卷的稳健性 (CS)

最近，人们对在不规则采样的点云上进行卷积产生了极大的兴趣。由于点云与常规栅格图像有很大的不同，因此必须更仔细地研究卷积网络的通用性，特别是它们在输入数据的尺度和旋转变化下的鲁棒性。本文研究了点云上卷积网络PointConv的不同变体，以考察其对输入尺度和旋转变化的鲁棒性。在我们探索的变体中，有两个变体是新颖的，并产生了显著的改进。第一个是用简单得多的三阶多项式替换了基于多层感知器的权重函数，同时采用了Sobolev规范化。其次，对于3D数据集，除了常规的3D坐标外，我们还利用3D几何属性作为PointConv的输入，从而推导出一种新型的观点不变的描述符。我们还探讨了激活函数、邻域和子采样方法的选择。我们在2D MNIST和CIFAR-10数据集以及3D SemanticKITTI和ScanNet数据集上进行了实验。结果显示，在2D上，使用三阶多项式极大地提高了PointConv对尺度变化和旋转的鲁棒性，甚至超过了MNIST数据集的传统2D CNN。在3D数据集上，新颖的视点不变量描述符显著提高了PointConv的性能以及鲁棒性。我们在SemanticKITTI数据集上实现了最先进的语义分割性能，在ScanNet数据集上也实现了与目前基于点的方法中最高框架的性能相当。

原文：Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, it is imperative to study the generalization of the convolution networks more closely, especially their robustness under variations in scale and rotations of the input data. This paper investigates different variants of PointConv, a convolution network on point clouds, to examine their robustness to input scale and rotation changes. Of the variants we explored, two are novel and generated significant improvements. The first is replacing the multilayer perceptron based weight function with much simpler third degree polynomials, together with a Sobolev norm regularization. Secondly, for 3D datasets, we derive a novel viewpoint-invariant descriptor by utilizing 3D geometric properties as the input to PointConv, in addition to the regular 3D coordinates. We have also explored choices of activation functions, neighborhood, and subsampling methods. Experiments are conducted on the 2D MNIST & CIFAR-10 datasets as well as the 3D SemanticKITTI & ScanNet datasets. Results reveal that on 2D, using third degree polynomials greatly improves PointConv's robustness to scale changes and rotations, even surpassing traditional 2D CNNs for the MNIST dataset. On 3D datasets, the novel viewpoint-invariant descriptor significantly improves the performance as well as robustness of PointConv. We achieve the state-of-the-art semantic segmentation performance on the SemanticKITTI dataset, as well as comparable performance with the current highest framework on the ScanNet dataset among point-based approaches.