Abstract

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 trained on uniform densities, we propose novel set\nlearning layers to adaptively combine features from multiple scales.\nExperiments show that our network called PointNet++ is able to learn deep point\nset features efficiently and robustly. In particular, results significantly\nbetter than state-of-the-art have been obtained on challenging benchmarks of 3D\npoint clouds.\n

Keywords

Artificial intelligenceFeature (linguistics)Metric (unit)Point (geometry)Space (punctuation)Computer scienceMetric spacePattern recognition (psychology)MathematicsGeometryPure mathematicsEngineering

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Year
2017
Type
preprint
Citations
7016
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Charles R. Qi, Yi Li, Hao Su et al. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric\n Space. arXiv (Cornell University) . https://doi.org/10.48550/arxiv.1706.02413

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DOI
10.48550/arxiv.1706.02413