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pointnet ++: deep Hierarchical Feature Learning onPoint Sets in a Metric SpaceCharles R. Qi Li Yi Hao Su Leonidas 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. By exploiting metric space distances, our network is able to learnlocal features with increasing contextual scales.
We will introduce a hierarchical feature learning framework in the next section to resolve the limitation. 3.2 Hierarchical Point Set Feature Learning While PointNet uses a single max pooling operation to aggregate the whole point set, our new architecture builds a hierarchical grouping of points and progressively abstract larger and larger local
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