PointWise:An Unsupervised Point-wise Feature Learning Network
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The availability and plethora of unlabeled point-clouds as well as their possible applications make finding ways of characterizing this type of data appealing. Previous research focused on describing entire point-clouds representing an object in a meaningful manner. We present a deep learning framework to learn point-wise description from a set of shapes without supervision. Our approach leverages self-supervision to define a relevant loss function to learn rich per-point features. We use local structures of point-clouds to incorporate geometric information into each point's latent representation. In addition to using local geometric information, we encourage adjacent points to have similar representations and vice-versa, creating a smoother, more descriptive representation. We demonstrate the ability of our method to capture meaningful point-wise features by clustering the learned embedding space to perform unsupervised part-segmentation on point clouds.
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