Open AccessProceedings Article
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles R. Qi,Li Yi,Hao Su,Leonidas J. Guibas +3 more
- Vol. 30, pp 5099-5108
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TLDR
PointNet++ as discussed by the authors applies PointNet recursively on a nested partitioning of the input point set to learn local features with increasing contextual scales, and proposes novel set learning layers to adaptively combine features from multiple scales.Abstract:
Few prior works study deep learning on point sets. PointNet is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.read more
Citations
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Proceedings ArticleDOI
SO-Net: Self-Organizing Network for Point Cloud Analysis
TL;DR: In this article, a self-organizing map (SOM) is proposed to model the spatial distribution of point clouds and perform hierarchical feature extraction on individual points and SOM nodes, and ultimately represent the input point cloud by a single feature vector.
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SuperGlue: Learning Feature Matching With Graph Neural Networks
TL;DR: SuperGlue is introduced, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points and introduces a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly.
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SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
TL;DR: This work proposes a novel convolutional architecture, termed SpiderCNN, to efficiently extract geometric features from point clouds, which inherits the multi-scale hierarchical architecture from the classical CNNs, which allows it to extract semantic deep features.
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STD: Sparse-to-Dense 3D Object Detector for Point Cloud
TL;DR: Wang et al. as discussed by the authors proposed a two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD), which uses raw point clouds as input to generate accurate proposals by seeding each point with a new spherical anchor.
Posted Content
SuperGlue: Learning Feature Matching with Graph Neural Networks
TL;DR: SuperGlue as discussed by the authors matches two sets of local features by jointly finding correspondences and rejecting non-matchable points by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network.