Fetching the paper…
Reading the bibliography…
Manipulating volumetric deformable objects in the real world, like plush toys and pizza dough, bring substantial challenges due to infinite shape variations, non-rigid motions, and partial observability.
A new measure of rank correlation
Maurice G Kendall · 1938
Earlier work this paper cites.
Deformable models
Demetri Terzopoulos and Kurt Fleischer · 1988
Earlier work this paper cites.
Dynamic 3d models with local and global deformations: deformable superquadrics
Demetri Terzopoulos and Dimitri Metaxas · 1991
Earlier work this paper cites.
Industrial applications of automatic manipulation of flexible materials
Mozafar Saadat and Ping Nan · 2002
Earlier work this paper cites.
Path planning for deformable linear objects
Mark Moll and Lydia E Kavraki · 2006
Earlier work this paper cites.
Physically based deformable models in computer graphics
Andrew Nealen, Matthias Müller, Richard Keiser, Eddy Boxerman, and Mark Carlson · 2006
Earlier work this paper cites.
A survey of computer-based deformable models
Patricia Moore and Derek Molloy · 2007
Earlier work this paper cites.
Position based dynamics
Matthias Müller, Bruno Heidelberger, Marcus Hennix, and John Ratcliff · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Fast point feature histograms (fpfh) for 3d registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz · 2009
Earlier work this paper cites.
Ros: an open-source robot operating system
Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, Andrew Y Ng, et al · 2009
Earlier work this paper cites.
Dexterous robotic manipulation of deformable objects with multi-sensory feedback-a review
Fouad F Khalil and Pierre Payeur · 2010
Earlier work this paper cites.
A survey on shape correspondence
Oliver Van Kaick, Hao Zhang, Ghassan Hamarneh, and Daniel Cohen-Or · 2011
Earlier work this paper cites.
Survey on model-based manipulation planning of deformable objects
P Jiménez · 2012
Earlier work this paper cites.
A geometric approach to robotic laundry folding
Stephen Miller, Jur Van Den Berg, Mario Fritz, Trevor Darrell, Ken Goldberg, and Pieter Abbeel · 2012
Earlier work this paper cites.
Tracking deformable objects with point clouds
John Schulman, Alex Lee, Jonathan Ho, and Pieter Abbeel · 2013
Earlier work this paper cites.
Real-time pose estimation of deformable objects using a volumetric approach
Yinxiao Li, Yan Wang, Michael Case, Shih-Fu Chang, and Peter K Allen · 2014
Earlier work this paper cites.
Recognition of deformable object category and pose
Yinxiao Li, Chih-Fan Chen, and Peter K Allen · 2014
Earlier work this paper cites.
On the visual deformation servoing of compliant objects: Uncalibrated control methods and experiments
David Navarro-Alarcon, Yun-hui Liu, Jose Guadalupe Romero, and Peng Li · 2014
Earlier work this paper cites.
Unified particle physics for real-time applications
Miles Macklin, Matthias Müller, Nuttapong Chentanez, and Tae-Yong Kim · 2014
Earlier work this paper cites.
Manipulation planning with contacts for an extensible elastic rod by sampling on the submanifold of static equilibrium configurations
Olivier Roussel, Andy Borum, Michel Taix, and Timothy Bretl · 2015
Earlier work this paper cites.
Pose and category recognition of highly deformable objects using deep learning
Ioannis Mariolis, Georgia Peleka, Andreas Kargakos, and Sotiris Malassiotis · 2015
Earlier work this paper cites.
Real-time tracking of 3d elastic objects with an rgb-d sensor
Antoine Petit, Vincenzo Lippiello, and Bruno Siciliano · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Smpl: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2015
Earlier work this paper cites.
Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx
Tom Erez, Yuval Tassa, and Emanuel Todorov · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Automatic 3-d manipulation of soft objects by robotic arms with an adaptive deformation model
David Navarro-Alarcon, Hiu Man Yip, Zerui Wang, Yun-Hui Liu, Fangxun Zhong, Tianxue Zhang, and Peng Li · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter W Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 2016
Earlier work this paper cites.
State estimation for deformable objects by point registration and dynamic simulation
Te Tang, Yongxiang Fan, Hsien-Chung Lin, and Masayoshi Tomizuka · 2017
Cited alongside, same era.
Fourier-based shape servoing: A new feedback method to actively deform soft objects into desired 2-d image contours
David Navarro-Alarcon and Yun-Hui Liu · 2017
Cited alongside, same era.
Se3-nets: Learning rigid body motion using deep neural networks
Arunkumar Byravan and Dieter Fox · 2017
Cited alongside, same era.
Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
Cited alongside, same era.
Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
What do single-view 3d reconstruction networks learn?
Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox · 2019
Later among the works it cites.
Learning to manipulate deformable objects without demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, and Pieter Abbeel · 2019
Later among the works it cites.
Self-supervised learning of state estimation for manipulating deformable linear objects
Mengyuan Yan, Yilin Zhu, Ning Jin, and Jeannette Bohg · 2020
Later among the works it cites.
Shapeflow: Learnable deformations among 3d shapes
Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, and Leonidas Guibas · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Cited alongside, same era.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
Deep functional maps: Structured prediction for dense shape correspondence
Or Litany, Tal Remez, Emanuele Rodola, Alex Bronstein, and Michael Bronstein · 2017
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Cited alongside, same era.
Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey
Jose Sanchez, Juan-Antonio Corrales, Belhassen-Chedli Bouzgarrou, and Youcef Mezouar · 2018
Cited alongside, same era.
Model-driven feedforward prediction for manipulation of deformable objects
Yinxiao Li, Yan Wang, Yonghao Yue, Danfei Xu, Michael Case, Shih-Fu Chang, Eitan Grinspun, and Peter K Allen · 2018
Cited alongside, same era.
3d-oes: Viewpoint-invariant object-factorized environment simulators
Hsiao-Yu Fish Tung, Zhou Xian, Mihir Prabhudesai, Shamit Lal, and Katerina Fragkiadaki · 2020
Later among the works it cites.
Learning 3d dynamic scene representations for robot manipulation
Zhenjia Xu, Zhanpeng He, Jiajun Wu, and Shuran Song · 2020
Later among the works it cites.
Learning predictive representations for deformable objects using contrastive estimation
Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, and Lerrel Pinto · 2020
Later among the works it cites.
Latent space roadmap for visual action planning of deformable and rigid object manipulation
Martina Lippi, Petra Poklukar, Michael C Welle, Anastasiia Varava, Hang Yin, Alessandro Marino, and Danica Kragic · 2020
Later among the works it cites.
Causal discovery in physical systems from videos
Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, and Animesh Garg · 2020
Later among the works it cites.
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
Later among the works it cites.
Atlas: End-to-end 3d scene reconstruction from posed images
Zak Murez, Tarrence van As, James Bartolozzi, Ayan Sinha, Vijay Badrinarayanan, and Andrew Rabinovich · 2020
Later among the works it cites.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Later among the works it cites.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
Later among the works it cites.
Nasa neural articulated shape approximation
Boyang Deng, John P Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi · 2020
Later among the works it cites.
Recent advances in shape correspondence
Yusuf Sahillioğlu · 2020
Later among the works it cites.
Deep geometric functional maps: Robust feature learning for shape correspondence
Nicolas Donati, Abhishek Sharma, and Maks Ovsjanikov · 2020
Later among the works it cites.
Deep shells: Unsupervised shape correspondence with optimal transport
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, and Daniel Cremers · 2020
Later among the works it cites.
Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany · 2020
Later among the works it cites.
Softgym: Benchmarking deep reinforcement learning for deformable object manipulation
Xingyu Lin, Yufei Wang, Jake Olkin, and David Held · 2020
Later among the works it cites.
Threedworld: A platform for interactive multi-modal physical simulation
Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, et al · 2020
Later among the works it cites.
Learning rope manipulation policies using dense object descriptors trained on synthetic depth data
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan, Ashwin Balakrishna, Michael Laskey, Kevin Stone, Joseph E Gonzalez, and Ken Goldberg · 2020
Later among the works it cites.
Learning visible connectivity dynamics for cloth smoothing
Xingyu Lin, Yufei Wang, and David Held · 2021
Later among the works it cites.
Nerfies: Deformable neural radiance fields
Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla · 2021
Later among the works it cites.
D-nerf: Neural radiance fields for dynamic scenes
Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer · 2021
Later among the works it cites.
Visuospatial foresight for physical sequential fabric manipulation
Ryan Hoque, Daniel Seita, Ashwin Balakrishna, Aditya Ganapathi, Ajay Kumar Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, and Ken Goldberg · 2021
Later among the works it cites.
3d neural scene representations for visuomotor control
Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, and Antonio Torralba · 2021
Later among the works it cites.
Continuous geodesic convolutions for learning on 3d shapes
Zhangsihao Yang, Or Litany, Tolga Birdal, Srinath Sridhar, and Leonidas Guibas · 2021
Later among the works it cites.
Neuromorph: Unsupervised shape interpolation and correspondence in one go
Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, and Andrea Vedaldi · 2021
Later among the works it cites.
Plasticinelab: A soft-body manipulation benchmark with differentiable physics
Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B Tenenbaum, and Chuang Gan · 2021
Later among the works it cites.
Defgraspsim: Simulation-based grasping of 3d deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner, Balakumar Sundaralingam, Miles Macklin, Tucker Hermans, and Dieter Fox · 2021
Later among the works it cites.