PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric\n Space

Few prior works study deep learning on point sets. PointNet by Qi et al. is a\npioneer in this direction. However, by design PointNet does not capture local\nstructures induced by the metric space points live in, limiting its ability to\nrecognize fine-grained patterns and generalizability to complex scenes. In this\nwork, we introduce a hierarchical neural network that applies PointNet\nrecursively on a nested partitioning of the input point set. By exploiting\nmetric space distances, our network is able to learn local features with\nincreasing contextual scales. With further observation that point sets are\nusually sampled with varying densities, which results in greatly decreased\nperformance for networks…