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Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments.
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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An iterative image registration technique with an application to stereo vision
Bruce D Lucas, Takeo Kanade, et al · 1981
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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, Jianxiong Su, Li Su, and Fisher Yu · 2015
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Rearrangement planning using object-centric and robot-centric action spaces
Jennifer E. King, Marco Cognetti, and Siddhartha S. Srinivasa · 2016
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Bounding boxes, segmentations and object coordinates: How important is recognition for 3D scene flow estimation in autonomous driving scenarios?
Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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FlowNet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Dex-Net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
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Embodied question answering
Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra · 2018
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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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Learning rigidity in dynamic scenes with a moving camera for 3D motion field estimation
Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli, Deqing Sun, James M. Rehg, and Jan Kautz · 2018
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Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
Douglas Morrison, Juxi Leitner, and Peter Corke · 2018
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PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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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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Gibson Env: Real-world perception for embodied agents
Fei Xia, Amir R. Zamir, Zhiyang He, Alexander Sax, Jitendra Malik, and Silvio Savarese · 2018
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Learning to find good correspondences
Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua · 2018
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Segmenting unknown 3D objects from real depth images using Mask R-CNN trained on synthetic data
Michael Danielczuk, Matthew Matl, Saurabh Gupta, Andrew Li, Andrew Lee, Jeffrey Mahler, and Ken Goldberg · 2019
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D2-Net: A trainable CNN for joint description and detection of local features
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler · 2019
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HPLFlowNet: Hierarchical permutohedral lattice FlowNet for scene flow estimation on large-scale point clouds
Xiuye Gu, Yijie Wang, Chongruo Wu, Yong Jae Lee, and Panqu Wang · 2019
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Iterative residual refinement for joint optical flow and occlusion estimation
Junhwa Hur and Stefan Roth · 2019
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SENSE: A shared encoder network for scene-flow estimation
Huaizu Jiang, Deqing Sun, Varun Jampani, Zhaoyang Lv, Erik Learned-Miller, and Jan Kautz · 2019
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FlowNet3D: Learning scene flow in 3D point clouds
Xingyu Liu, Charles R. Qi, and Leonidas J. Guibas · 2019
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Deep rigid instance scene flow
Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, and Raquel Urtasun · 2019
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6-DOF GraspNet: Variational grasp generation for object manipulation
Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
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PVNet: Pixel-wise voting network for 6DoF pose estimation
Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, and Hujun Bao · 2019
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Habitat: A platform for embodied AI research
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DenseFusion: 6D object pose estimation by iterative dense fusion
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martin-Martin, Cewu Lu, Li Fei-Fei, and Silvio Savarese · 2019
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Rearrangement: A challenge for embodied AI
Dhruv Batra, Angel X. Chang, Sonia Chernova, Andrew J. Davison, Jia Deng, Vladlen Koltun, Sergey Levine, Jitendra Malik, Igor Mordatch, Roozbeh Mottaghi, Manolis Savva, and Hao Su · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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ManipulaTHOR: A framework for visual object manipulation
Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, and Roozbeh Mottaghi · 2021
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Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
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Weakly supervised learning of rigid 3D scene flow
Zan Gojcic, Or Litany, Andreas Wieser, Leonidas J. Guibas, and Tolga Birdal · 2021
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Google scanned objects
Google Research · 2021
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Self-supervised multi-frame monocular scene flow
Junhwa Hur and Stefan Roth · 2021
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Learning to estimate hidden motions with global motion aggregation
Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley · 2021
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Object rearrangement using learned implicit collision functions
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RoboTHOR: An open simulation-to-real embodied AI platform
Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, and Ali Farhadi · 2020
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Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image
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Online replanning in belief space for partially observable task and motion problems
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PackIt: A virtual environment for geometric planning
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Rel3D: A minimally contrastive benchmark for grounding spatial relations in 3D
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Self-supervised monocular scene flow estimation
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StructFormer: Learning spatial structure for language-guided semantic rearrangement of novel objects
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Swin transformer: Hierarchical vision transformer using shifted windows
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OCRTOC: A cloud-based competition and benchmark for robotic grasping and manipulation
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UPFlow: Upsampling pyramid for unsupervised optical flow learning
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NViSII: A scriptable tool for photorealistic image generation
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Chris Paxton, Chris Xie, Tucker Hermans, and Dieter Fox · 2021
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NeRP: Neural rearrangement planning for unknown objects
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Learning to rearrange deformable cables, fabrics, and bags with goal-conditioned transporter networks
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CLIPort: What and where pathways for robotic manipulation
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SMURF: Self-teaching multi-frame unsupervised RAFT with full-image warping
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AutoFlow: Learning a better training set for optical flow
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Contact-GraspNet: Efficient 6-DoF grasp generation in cluttered scenes
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RAFT-3D: Scene flow using rigid-motion embeddings
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Visual room rearrangement
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