PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling

Understand

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.