Point Cloud Models
PointASNL
Introduced by Yan et al. in PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive SamplingPointASNL is a non-local neural network for point clouds processing It consists of two general modules: adaptive sampling (AS) module and local-Nonlocal (L-NL) module. The AS module 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. The AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. The L-NL module capture the neighbor and long-range dependencies of the sampled point, and enables the learning process to be insensitive to noise.
Source: PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Papers
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