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The ability to grasp and manipulate transparent objects is a major challenge for robots.
Robotic grasping and contact: A review
A. Bicchi and V. Kumar · 2000
Earlier work this paper cites.
Springer handbook of robotics
D. Prattichizzo, J. C. Trinkle, B. Siciliano, and O. Khatib · 2008
Earlier work this paper cites.
Transparent object detection using regions with convolutional neural network
P.-J. Lai and C.-S. Fuh · 2015
Earlier work this paper cites.
Leveraging big data for grasp planning
D. Kappler, J. Bohg, and S. Schaal · 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.
Real-time grasp detection using convolutional neural networks
J. Redmon and A. Angelova · 2015
Earlier work this paper cites.
Structure-from-motion revisited
J. L. Schönberger and J.-M. Frahm · 2016
Earlier work this paper cites.
Pixelwise view selection for unstructured multi-view stereo
J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm · 2016
Earlier work this paper cites.
Seeing glassware: from edge detection to pose estimation and shape recovery
C. J. Phillips, M. Lecce, and K. Daniilidis · 2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
A mathematical introduction to robotic manipulation
R. M. Murray, Z. Li, and S. S. Sastry · 2017
Earlier work this paper cites.
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg · 2017
Earlier work this paper cites.
Transparent object detection using convolutional neural network
M. P. Khaing and M. Masayuki · 2018
Earlier work this paper cites.
Grasp2vec: Learning object representations from self-supervised grasping
E. Jang, C. Devin, V. Vanhoucke, and S. Levine · 2018
Cited alongside, same era.
Dex-net 3.0: Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning
J. Mahler, M. Matl, X. Liu, A. Li, D. Gealy, and K. Goldberg · 2018
Cited alongside, same era.
Densefusion: 6d object pose estimation by iterative dense fusion
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese · 2019
Cited alongside, same era.
Glassloc: Plenoptic grasp pose detection in transparent clutter
Z. Zhou, T. Pan, S. Wu, H. Chang, and O. C. Jenkins · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
Cited alongside, same era.
Jaxnerf: an efficient jax implementation of nerf, 2020, 2020
J. T. B. Boyang Deng and P. P. Srinivasan · 2020
Later among the works it cites.
A survey on learning-based robotic grasping
K. Kleeberger, R. Bormann, W. Kraus, and M. F. Huber · 2020
Later among the works it cites.
S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes
Y. Qin, R. Chen, H. Zhu, M. Song, J. Xu, and H. Su · 2020
Later among the works it cites.
Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
S. Song, A. Zeng, J. Lee, and T. Funkhouser · 2020
Later among the works it cites.
Segmenting transparent object in the wild with transformer
E. Xie, W. Wang, W. Wang, P. Sun, H. Xu, D. Liang, and P. Luo · 2021
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Rgb-d local implicit function for depth completion of transparent objects
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Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
Cited alongside, same era.
Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
Cited alongside, same era.
Scene representation networks: Continuous 3d-structure-aware neural scene representations
V. Sitzmann, M. Zollhöfer, and G. Wetzstein · 2019
Cited alongside, same era.
6-dof graspnet: Variational grasp generation for object manipulation
A. Mousavian, C. Eppner, and D. Fox · 2019
Cited alongside, same era.
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
Cited alongside, same era.
6dof pose estimation of transparent object from a single rgb-d image
C. Xu, J. Chen, M. Yao, J. Zhou, L. Zhang, and Y. Liu · 2020
Cited alongside, same era.
Clear grasp: 3d shape estimation of transparent objects for manipulation
S. Sajjan, M. Moore, M. Pan, G. Nagaraja, J. Lee, A. Zeng, and S. Song · 2020
Cited alongside, same era.
L. Zhu, A. Mousavian, Y. Xiang, H. Mazhar, J. van Eenbergen, S. Debnath, and D. Fox · 2021
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Plenoctrees for real-time rendering of neural radiance fields
A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa · 2021
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Depth-supervised nerf: Fewer views and faster training for free
K. Deng, A. Liu, J.-Y. Zhu, and D. Ramanan · 2021
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A self-supervised learning-based 6-dof grasp planning method for manipulator
G. Peng, Z. Ren, H. Wang, and X. Li · 2021
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox · 2021
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Avplug: Approach vector planning for unicontact grasping amid clutter
Y. Avigal, V. Satish, Z. Tam, H. Huang, H. Zhang, M. Danielczuk, J. Ichnowski, and K. Goldberg · 2021
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Volumetric grasping network: Real-time 6 dof grasp detection in clutter
M. Breyer, J. J. Chung, L. Ott, R. Siegwart, and J. Nieto · 2021
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