The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions

Understand

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.