Transcription of PointNet++: Deep Hierarchical Feature Learning on Point ...
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PointNet++: Deep Hierarchical Feature Learning on Point ...
pointnet ++: Deep Hierarchical Feature Learning onPoint Sets in a Metric SpaceCharles R. QiLi YiHao SuLeonidas J. GuibasStanford UniversityAbstractFew prior works study deep Learning on Point sets. pointnet [20] is a pioneer in thisdirection. However, by design pointnet does not capture local structures induced bythe metric space points live in, limiting its ability to recognize fine-grained patternsand generalizability to complex scenes. In this work, we introduce a hierarchicalneural network that applies pointnet recursively on a nested partitioning of theinput Point set.
A set abstraction level takes an N (d+ C) matrix as input that is from Npoints with d-dim coordinates and C-dim point feature. It outputs an N0 0(d+C) matrix of N0subsampled points with d-dim coordinates and new C0-dim feature vectors summarizing local context. We introduce the layers of a set abstraction level in the following paragraphs ...
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