Qi Charles R. Yi Li Su Hao Guibas Leonidas J.

Abstract

Few prior works study deep learning on point sets. PointNet by Qi et al. is apioneer in this direction. However, by design PointNet does not capture localstructures induced by the metric space points live in, limiting its ability torecognize fine-grained patterns and generalizability to complex scenes. In thiswork, we introduce a hierarchical neural network that applies PointNetrecursively on a nested partitioning of the input point set. By exploitingmetric space distances, our network is able to learn local features withincreasing contextual scales. With further observation that point sets areusually sampled with varying densities, which results in greatly decreasedperformance for networks trained on uniform densities, we propose novel setlearning layers to adaptively combine features from multiple scales.Experiments show that our network called PointNet++ is able to learn deep pointset features efficiently and robustly. In particular, results significantlybetter than state-of-the-art have been obtained on challenging benchmarks of 3Dpoint clouds.