Self-Sampling for Neural Point Cloud Consolidation. (arXiv:2008.06471v3 [cs.GR] UPDATED)

We introduce a novel technique for neural point cloud consolidation which
learns from only the input point cloud. Unlike other point upsampling methods
which analyze shapes via local patches, in this work, we learn from global
subsets. We repeatedly self-sample the input point cloud with global subsets
that are used to train a deep neural network. Specifically, we define source
and target subsets according to the desired consolidation criteria (e.g.,
generating sharp points or points in sparse regions). The network …

arxiv cloud consolidation sampling