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Charles R. Qi
Researcher at Facebook
Publications - 43
Citations - 19659
Charles R. Qi is an academic researcher from Facebook. The author has contributed to research in topics: Point cloud & Computer science. The author has an hindex of 24, co-authored 31 publications receiving 12970 citations. Previous affiliations of Charles R. Qi include Stanford University.
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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TL;DR: A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.
Proceedings Article
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TL;DR: 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.
Proceedings ArticleDOI
Frustum PointNets for 3D Object Detection from RGB-D Data
TL;DR: This work directly operates on raw point clouds by popping up RGBD scans and leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects.
Proceedings ArticleDOI
KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas,Charles R. Qi,Jean-Emmanuel Deschaud,Beatriz Marcotegui,François Goulette,Leonidas J. Guibas +5 more
TL;DR: KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.
Proceedings ArticleDOI
Volumetric and Multi-view CNNs for Object Classification on 3D Data
TL;DR: In this paper, two distinct network architectures of volumetric CNNs and multi-view CNNs are introduced, where they introduce multiresolution filtering in 3D. And they provide extensive experiments designed to evaluate underlying design choices.