  Raw point clouds data inevitably contains outliers or noise through
acquisition from 3D sensors or reconstruction algorithms. In this paper, we
present a novel end-to-end network for robust point clouds processing, named
PointASNL, which can deal with point clouds with noise effectively. The key
component in our approach is the adaptive sampling (AS) module. It first
re-weights the neighbors around the initial sampled points from farthest point
sampling (FPS), and then adaptively adjusts the sampled points beyond the
entire point cloud. Our AS module can not only benefit the feature learning of
point clouds, but also ease the biased effect of outliers. To further capture
the neighbor and long-range dependencies of the sampled point, we proposed a
local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL
module enables the learning process insensitive to noise. Extensive experiments
verify the robustness and superiority of our approach in point clouds
processing tasks regardless of synthesis data, indoor data, and outdoor data
with or without noise. Specifically, PointASNL achieves state-of-the-art robust
performance for classification and segmentation tasks on all datasets, and
significantly outperforms previous methods on real-world outdoor SemanticKITTI
dataset with considerate noise. Our code is released through
https://github.com/yanx27/PointASNL.
