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3D neural networks have become prevalent for many 3D vision tasks including object detection, segmentation, registration, and various perception tasks for 3D inputs.
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Channel pruning for accelerating very deep neural networks
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Pytorch: An imperative style, high-performance deep learning library
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Pointconv: Deep convolutional networks on 3d point clouds
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Deep global registration
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Deep hough voting for 3d object detection in point clouds
Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas · 2019
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Occupancy networks: Learning 3d reconstruction in function space
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Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
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Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
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Dynamic graph cnn for learning on point clouds
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Scalability in perception for autonomous driving: Waymo open dataset
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Searching efficient 3d architectures with sparse point-voxel convolution
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Picking winning tickets before training by preserving gradient flow
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel LK Yamins, and Surya Ganguli · 2020
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Pruning 3d filters for accelerating 3d convnets
Zhenzhen Wang, Weixiang Hong, Yap-Peng Tan, and Junsong Yuan · 2020
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Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling
Xu Yan, Chaoda Zheng, Zhen Li, Sheng Wang, and Shuguang Cui · 2020
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi · 2021
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Mix3d: Out-of-context data augmentation for 3d scenes
Alexey Nekrasov, Jonas Schult, Or Litany, Bastian Leibe, and Francis Engelmann · 2021
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