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We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data with no prior knowledge of visual ambiguities like symmetry.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Model globally, match locally: Efficient and robust 3d object recognition
Bertram Drost, Markus Ulrich, Nassir Navab, and Slobodan Ilic · 2010
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Efficient and flexible sampling with blue noise properties of triangular meshes
Massimiliano Corsini, Paolo Cignoni, and Roberto Scopigno · 2012
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Learning 6d object pose estimation using 3d object coordinates
Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Recovering 6d object pose and predicting next-best-view in the crowd
Andreas Doumanoglou, Rigas Kouskouridas, Sotiris Malassiotis, and Tae-Kyun Kim · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Bertram Drost, Markus Ulrich, Paul Bergmann, Philipp Hartinger, and Carsten Steger · 2017
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T-less: An rgb-d dataset for 6d pose estimation of texture-less objects
Tomáš Hodan, Pavel Haluza, Štepán Obdržálek, Jiri Matas, Manolis Lourakis, and Xenophon Zabulis · 2017
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An efficient algebraic solution to the perspective-three-point problem
Tong Ke and Stergios I Roumeliotis · 2017
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Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth
Mahdi Rad and Vincent Lepetit · 2017
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Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox · 2017
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Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
Peter R Florence, Lucas Manuelli, and Russ Tedrake · 2018
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Deepim: Deep iterative matching for 6d pose estimation
Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox · 2018
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
L. McInnes, J. Healy, and J. Melville · 2018
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Epos: Estimating 6d pose of objects with symmetries
Tomas Hodan, Daniel Barath, and Jiri Matas · 2020
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Bop challenge 2020 on 6d object localization
Tomáš Hodaň, Martin Sundermeyer, Bertram Drost, Yann Labbé, Eric Brachmann, Frank Michel, Carsten Rother, and Jiří Matas · 2020
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A hybrid approach for 6dof pose estimation
Rebecca König and Bertram Drost · 2020
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Cosypose: Consistent multi-view multi-object 6d pose estimation
Yann Labbé, Justin Carpentier, Mathieu Aubry, and Josef Sivic · 2020
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Continuous surface embeddings
Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec, Patrick Labatut, and Andrea Vedaldi · 2020
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Representation learning with contrastive predictive coding
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Implicit 3d orientation learning for 6d object detection from rgb images
Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, and Rudolph Triebel · 2018
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A method for 6d pose estimation of free-form rigid objects using point pair features on range data
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3d object detection and pose estimation of unseen objects in color images with local surface embeddings
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Implicit neural representations with periodic activation functions
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Multi-path learning for object pose estimation across domains
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Poserbpf: A rao–blackwellized particle filter for 6-d object pose tracking
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