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We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Natural terrain classification using three-dimensional ladar data for ground robot mobility
Jean-François Lalonde, Nicolas Vandapel, Daniel F. Huber, and Martial Hebert · 2006
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3d convolutional neural networks for human action recognition
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Jincheng Yu, Kaijian Weng, Guoyuan Liang, and Guanghan Xie · 2013
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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3d shapenets: A deep representation for volumetric shapes
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2016 12th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)
Applying deep learning in augmented reality tracking · 2016
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Monocular 3d object detection for autonomous driving
Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2016
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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Real-time high resolution 3d data on the hololens
Mathieu Garon, Pierre-Olivier Boulet, Jean-Philippe Doironz, Luc Beaulieu, and Jean-François Lalonde · 2016
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gvnn: Neural network library for geometric computer vision
Ankur Handa, Michael Bloesch, Viorica Pătrăucean, Simon Stent, John McCormac, and Andrew Davison · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T Freeman, and Joshua B Tenenbaum · 2016
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Real-time 3d scene layout from a single image using convolutional neural networks
Shichao Yang, Daniel Maturana, and Sebastian Scherer · 2016
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Joint 2D-3D-Semantic Data for Indoor Scene Understanding
I. Armeni, A. Sax, A. R. Zamir, and S. Savarese · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Convolutional neural networks on surfaces via seamless toric covers
Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G Kim, and Yaron Lipman · 2017
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3d bounding box estimation using deep learning and geometry
Arsalan Mousavian, Dragomir Anguelov, John Flynn, and Jana Kosecka · 2017
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
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Learning to predict 3d objects with an interpolation-based differentiable renderer
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Kornia: an open source differentiable computer vision library for pytorch
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2019 tutorial on 3d deep learning
Hao Su · 2019
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Soft rasterizer: A differentiable renderer for image-based 3d reasoning
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Automatic differentiation in PyTorch
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Improved adversarial systems for 3d object generation and reconstruction
Edward J Smith and David Meger · 2017
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Neural 3d mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Differentiable monte-carlo ray tracing through edge sampling
Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen · 2018
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Visual slam for automated driving: Exploring the applications of deep learning
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Shichen Liu, Tianye Li, Weikai Chen, and Hao Li · 2019
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Occupancy networks: Learning 3d reconstruction in function space
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PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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