Fetching the paper…
Reading the bibliography…
Grasping objects by a specific part is often crucial for safety and for executing downstream tasks.
An efficient algorithm for co-segmentation
D. S. Hochbaum and V. Singh · 2009
Earlier work this paper cites.
Bicos: A bi-level co-segmentation method for image classification
Y. Chai, V. Lempitsky, and A. Zisserman · 2011
Earlier work this paper cites.
Microsoft coco: Common objects in context, 2015
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár · 2015
Earlier work this paper cites.
Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
Earlier work this paper cites.
The ycb object and model set: Towards common benchmarks for manipulation research
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar · 2015
Earlier work this paper cites.
Task-based robot grasp planning using probabilistic inference
D. Song, C. H. Ek, K. Huebner, and D. Kragic · 2015
Earlier work this paper cites.
Affordance detection of tool parts from geometric features
A. Myers, C. L. Teo, C. Fermüller, and Y. Aloimonos · 2015
Earlier work this paper cites.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
Earlier work this paper cites.
Part-based grasp planning for familiar objects
N. Vahrenkamp, L. Westkamp, N. Yamanobe, E. E. Aksoy, and T. Asfour · 2016
Earlier work this paper cites.
Using simulation and domain adaptation to improve efficiency of deep robotic grasping, 2017
K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, S. Levine, and V. Vanhoucke · 2017
Earlier work this paper cites.
Domain randomization for transferring deep neural networks from simulation to the real world, 2017
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
Earlier work this paper cites.
Task-oriented grasping with semantic and geometric scene understanding
R. Detry, J. Papon, and L. Matthies · 2017
Earlier work this paper cites.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation, 2018
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine · 2018
Earlier work this paper cites.
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
Earlier work this paper cites.
Jacquard: A large scale dataset for robotic grasp detection
A. Depierre, E. Dellandréa, and L. Chen · 2018
Earlier work this paper cites.
Mattnet: Modular attention network for referring expression comprehension
L. Yu, Z. Lin, X. Shen, J. Yang, X. Lu, M. Bansal, and T. L. Berg · 2018
Earlier work this paper cites.
6-dof graspnet: Variational grasp generation for object manipulation, 2019
A. Mousavian, C. Eppner, and D. Fox · 2019
Earlier work this paper cites.
Learning ambidextrous robot grasping policies
J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg · 2019
Earlier work this paper cites.
Learning affordance segmentation for real-world robotic manipulation via synthetic images
F.-J. Chu, R. Xu, and P. A. Vela · 2019
Earlier work this paper cites.
Single shot 6d object pose estimation, 2020
K. Kleeberger and M. F. Huber · 2020
Earlier work this paper cites.
Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
S. Song, A. Zeng, J. Lee, and T. Funkhouser · 2020
Earlier work this paper cites.
Graspnet-1billion: A large-scale benchmark for general object grasping
H.-S. Fang, C. Wang, M. Gou, and C. Lu · 2020
Earlier work this paper cites.
Same object, different grasps: Data and semantic knowledge for task-oriented grasping
A. Murali, W. Liu, K. Marino, S. Chernova, and A. Gupta · 2020
Earlier work this paper cites.
Learning task-oriented grasping from human activity datasets
M. Kokic, D. Kragic, and J. Bohg · 2020
Earlier work this paper cites.
Robust task-based grasping as a service
J. Song, A. Tanwani, J. Ichnowski, M. Danielczuk, K. Sanders, J. Chui, J. A. Ojea, and K. Goldberg · 2020
Earlier work this paper cites.
NeRF: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
Earlier work this paper cites.
D-NeRF: Neural Radiance Fields for Dynamic Scenes
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer · 2020
Earlier work this paper cites.
Dex-NeRF: Using a neural radiance field to grasp transparent objects
J. Ichnowski*, Y. Avigal*, J. Kerr, and K. Goldberg · 2020
Earlier work this paper cites.
Learning transferable visual models from natural language supervision, 2021
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever · 2021
Earlier work this paper cites.
Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
Earlier work this paper cites.
Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan · 2021
Cited alongside, same era.
Plenoxels: Radiance fields without neural networks
A. Yu, S. Fridovich-Keil, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa · 2021
Cited alongside, same era.
Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields
K. Park, U. Sinha, P. Hedman, J. T. Barron, S. Bouaziz, D. B. Goldman, R. Martin-Brualla, and S. M. Seitz · 2021
Cited alongside, same era.
imap: Implicit mapping and positioning in real-time
E. Sucar, S. Liu, J. Ortiz, and A. J. Davison · 2021
Cited alongside, same era.
In-place scene labelling and understanding with implicit scene representation
S. Zhi, T. Laidlow, S. Leutenegger, and A. J. Davison · 2021
Cited alongside, same era.
Open-vocabulary object detection via vision and language knowledge distillation, 2022
X. Gu, T.-Y. Lin, W. Kuo, and Y. Cui · 2022
Later among the works it cites.
Detecting twenty-thousand classes using image-level supervision, 2022
X. Zhou, R. Girdhar, A. Joulin, P. Krähenbühl, and I. Misra · 2022
Later among the works it cites.
Simple open-vocabulary object detection with vision transformers, 2022
M. Minderer, A. Gritsenko, A. Stone, M. Neumann, D. Weissenborn, A. Dosovitskiy, A. Mahendran, A. Arnab, M. Dehghani, Z. Shen, X. Wang, X. Zhai, T. Kipf, and N. Houlsby · 2022
Later among the works it cites.
Visual language maps for robot navigation
C. Huang, O. Mees, A. Zeng, and W. Burgard · 2022
Later among the works it cites.
Openscene: 3d scene understanding with open vocabularies
S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, T. Funkhouser, et al · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Open-vocabulary object detection using captions, 2021
A. Zareian, K. D. Rosa, D. H. Hu, and S.-F. Chang · 2021
Cited alongside, same era.
Open-vocabulary image segmentation
G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin · 2021
Cited alongside, same era.
Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
H. Chefer, S. Gur, and L. Wolf · 2021
Cited alongside, same era.
Composing pick-and-place tasks by grounding language, 2021
O. Mees and W. Burgard · 2021
Cited alongside, same era.
Cliport: What and where pathways for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2021
Cited alongside, same era.
Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs, 2021
M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. M. Sajjadi, A. Geiger, and N. Radwan · 2021
Cited alongside, same era.
Deep vit features as dense visual descriptors
S. Amir, Y. Gandelsman, S. Bagon, and T. Dekel · 2022
Cited alongside, same era.
Later among the works it cites.
Clip-fields: Weakly supervised semantic fields for robotic memory
N. M. M. Shafiullah, C. Paxton, L. Pinto, S. Chintala, and A. Szlam · 2022
Later among the works it cites.
Open-vocabulary queryable scene representations for real world planning
B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler · 2022
Later among the works it cites.
Semantic abstraction: Open-world 3D scene understanding from 2D vision-language models
H. Ha and S. Song · 2022
Later among the works it cites.
Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
Later among the works it cites.
Rt-1: Robotics transformer for real-world control at scale
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al · 2022
Later among the works it cites.
Compositional visual generation with composable diffusion models
N. Liu, S. Li, Y. Du, A. Torralba, and J. B. Tenenbaum · 2022
Later among the works it cites.
Lerf: Language embedded radiance fields
J. Kerr, C. M. Kim, K. Goldberg, A. Kanazawa, and M. Tancik · 2023
Closest in time.
Locate: Localize and transfer object parts for weakly supervised affordance grounding
G. Li, V. Jampani, D. Sun, and L. Sevilla-Lara · 2023
Closest in time.
Learning 6-dof fine-grained grasp detection based on part affordance grounding
Y. Song, P. Sun, Y. Ren, Y. Zheng, and Y. Zhang · 2023
Closest in time.
Going denser with open-vocabulary part segmentation
P. Sun, S. Chen, C. Zhu, F. Xiao, P. Luo, S. Xie, and Z. Yan · 2023
Closest in time.
Robustnerf: Ignoring distractors with robust losses
S. Sabour, S. Vora, D. Duckworth, I. Krasin, D. J. Fleet, and A. Tagliasacchi · 2023
Closest in time.
Radiance field gradient scaling for unbiased near-camera training
J. Philip and V. Deschaintre · 2023
Closest in time.
Nerfstudio: A modular framework for neural radiance field development
M. Tancik, E. Weber, E. Ng, R. Li, B. Yi, J. Kerr, T. Wang, A. Kristoffersen, J. Austin, K. Salahi, et al · 2023
Closest in time.
F2-nerf: Fast neural radiance field training with free camera trajectories
P. Wang, Y. Liu, Z. Chen, L. Liu, Z. Liu, T. Komura, C. Theobalt, and W. Wang · 2023
Closest in time.
Zip-nerf: Anti-aliased grid-based neural radiance fields
J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman · 2023
Closest in time.
K-planes: Explicit radiance fields in space, time, and appearance
S. Fridovich-Keil, G. Meanti, F. R. Warburg, B. Recht, and A. Kanazawa · 2023
Closest in time.
Dynibar: Neural dynamic image-based rendering
Z. Li, Q. Wang, F. Cole, R. Tucker, and N. Snavely · 2023
Closest in time.
Open-vocabulary semantic segmentation with mask-adapted clip, 2023
F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu · 2023
Closest in time.
Conceptfusion: Open-set multimodal 3d mapping
K. Jatavallabhula, A. Kuwajerwala, Q. Gu, M. Omama, T. Chen, S. Li, G. Iyer, S. Saryazdi, N. Keetha, A. Tewari, J. Tenenbaum, C. de Melo, M. Krishna, L. Paull, F. Shkurti, and A. Torralba · 2023
Closest in time.
Conceptfusion: Open-set multimodal 3d mapping
K. M. Jatavallabhula, A. Kuwajerwala, Q. Gu, M. Omama, T. Chen, S. Li, G. Iyer, S. Saryazdi, N. Keetha, A. Tewari, et al · 2023
Closest in time.
Open-world object manipulation using pre-trained vision-language models
A. Stone, T. Xiao, Y. Lu, K. Gopalakrishnan, K.-H. Lee, Q. Vuong, P. Wohlhart, B. Zitkovich, F. Xia, C. Finn, et al · 2023
Closest in time.
Dinov2: Learning robust visual features without supervision
M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, et al · 2023
Closest in time.
Zoedepth: Zero-shot transfer by combining relative and metric depth
S. F. Bhat, R. Birkl, D. Wofk, P. Wonka, and M. Müller · 2023
Closest in time.
Sparsenerf: Distilling depth ranking for few-shot novel view synthesis
Guangcong, Z. Chen, C. C. Loy, and Z. Liu · 2023
Closest in time.