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The basis of many object manipulation algorithms is RGB-D input.
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 · 1901
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Cleargrasp: 3d shape estimation of transparent objects for manipulation
S. S. Sajjan, M. Moore, M. Pan, G. Nagaraja, J. Lee, A. Zeng, and S. Song · 1910
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Maskedfusion: Mask-based 6d object pose detection
N. Pereira and L. A. Alexandre · 1911
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Keypose: Multi-view 3d labeling and keypoint estimation for transparent objects
X. Liu, R. Jonschkowski, A. Angelova, and K. Konolige · 1912
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Transparent object detection and reconstruction on a mobile platform
U. Klank, D. Carton, and M. Beetz · 2011
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Indoor segmentation and support inference from rgbd images
P. K. Nathan Silberman, Derek Hoiem and R. Fergus · 2012
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Seeing the unseen: Simple reconstruction of transparent objects from point cloud data
S. Albrecht and S. Marsland · 2013
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Pose estimation of rigid transparent objects in transparent clutter
I. Lysenkov and V. Rabaud · 2013
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Depth map prediction from a single image using a multi-scale deep network, 2014
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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A fixed viewpoint approach for dense reconstruction of transparent objects
K. Han, K.-Y. K. Wong, and M. Liu · 2015
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U-net: Convolutional networks for biomedical image segmentation, 2015
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Imagenet large scale visual recognition challenge, 2015
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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3d reconstruction of transparent objects with position-normal consistency
Y. Qian, M. Gong, and Y.-H. Yang · 2016
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Seeing glassware: from edge detection to pose estimation and shape recovery
C. J. Phillips, M. Lecce, and K. Daniilidis · 2016
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Deeper depth prediction with fully convolutional residual networks, 2016
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
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Unsupervised cnn for single view depth estimation: Geometry to the rescue, 2016
R. Garg, V. K. BG, G. Carneiro, and I. Reid · 2016
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Apriltag 2: Efficient and robust fiducial detection
J. Wang and E. Olson · 2016
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Rethinking atrous convolution for semantic image segmentation, 2017
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Fusing depth and silhouette for scanning transparent object with rgb-d sensor
Y. Ji, Q. Xia, and Z. Zhang · 2017
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Depth completion for kinect v2 sensor
W. Song, L. Anh Vu, S. Yun, S.-W. Jung, and C. Won · 2017
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Single-image depth perception in the wild, 2017
W. Chen, Z. Fu, D. Yang, and J. Deng · 2017
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Unsupervised monocular depth estimation with left-right consistency, 2017
C. Godard, O. M. Aodha, and G. J. Brostow · 2017
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Multi-scale continuous crfs as sequential deep networks for monocular depth estimation, 2017
D. Xu, E. Ricci, W. Ouyang, X. Wang, and N. Sebe · 2017
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Sparsity invariant cnns, 2017
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger · 2017
Cited alongside, same era.
Adam: A method for stochastic optimization, 2017
D. P. Kingma and J. Ba · 2017
Cited alongside, same era.
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics, 2017
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg · 2017
S4g: Amodal single-view single-shot se(3) grasp detection in cluttered scenes, 2019
Y. Qin, R. Chen, H. Zhu, M. Song, J. Xu, and H. Su · 2019
Later among the works it cites.
Data-efficient learning for sim-to-real robotic grasping using deep point cloud prediction networks, 2019
X. Yan, M. Khansari, J. Hsu, Y. Gong, Y. Bai, S. Pirk, and H. Lee · 2019
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Segmenting transparent objects in the wild, 2020
E. Xie, W. Wang, W. Wang, M. Ding, C. Shen, and P. Luo · 2020
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End-to-end object detection with transformers, 2020
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
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Computer vision for recognition of materials and vessels in chemistry lab settings and the vector-labpics data set
S. Eppel, H. Xu, M. Bismuth, and A. Aspuru-Guzik · 2020
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Cited alongside, same era.
Grasp pose detection in point clouds, 2017
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt · 2017
Cited alongside, same era.
Mask r-cnn, 2018
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2018
Cited alongside, same era.
Tom-net: Learning transparent object matting from a single image, 2018
G. Chen, K. Han, and K.-Y. K. Wong · 2018
Cited alongside, same era.
Depth reconstruction of translucent objects from a single time-of-flight camera using deep residual networks, 2018
S. Song and H. Shim · 2018
Cited alongside, same era.
Detail preserving depth estimation from a single image using attention guided networks, 2018
Z. Hao, Y. Li, S. You, and F. Lu · 2018
Cited alongside, same era.
Structured attention guided convolutional neural fields for monocular depth estimation, 2018
D. Xu, W. Wang, H. Tang, H. Liu, N. Sebe, and E. Ricci · 2018
Cited alongside, same era.
Z. Zhou, X. Chen, and O. C. Jenkins · 2020
Later among the works it cites.
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
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer, 2020
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun · 2020
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Guiding monocular depth estimation using depth-attention volume, 2020
L. Huynh, P. Nguyen-Ha, J. Matas, E. Rahtu, and J. Heikkila · 2020
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From big to small: Multi-scale local planar guidance for monocular depth estimation, 2020
J. H. Lee, M.-K. Han, D. W. Ko, and I. H. Suh · 2020
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S3cnet: A sparse semantic scene completion network for lidar point clouds, 2020
R. Cheng, C. Agia, Y. Ren, X. Li, and L. Bingbing · 2020
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A modular robotic arm control stack for research: Franka-interface and frankapy, 2020
K. Zhang, M. Sharma, J. Liang, and O. Kroemer · 2020
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A mobile robotic chemist
B. Burger, P. Maffettone, V. Gusev, C. Aitchison, Y. Bai, X. Wang, X. Li, B. Alston, B. Li, R. Clowes, N. Rankin, B. Harris, R. Sprick, and A. Cooper · 2020
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Grnet: Gridding residual network for dense point cloud completion, 2020
H. Xie, H. Yao, S. Zhou, J. Mao, S. Zhang, and W. Sun · 2020
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Decoder modulation for indoor depth completion
A. K. Dmitry Senushkin, Ilia Belikov · 2020
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Non-local spatial propagation network for depth completion, 2020
J. Park, K. Joo, Z. Hu, C.-K. Liu, and I. S. Kweon · 2020
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Efficientnet: Rethinking model scaling for convolutional neural networks, 2020
M. Tan and Q. V. Le · 2020
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Segmenting transparent object in the wild with transformer, 2021
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, 2021
L. Zhu, A. Mousavian, Y. Xiang, H. Mazhar, J. van Eenbergen, S. Debnath, and D. Fox · 2021
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Regnet: Region-based grasp network for end-to-end grasp detection in point clouds, 2021
B. Zhao, H. Zhang, X. Lan, H. Wang, Z. Tian, and N. Zheng · 2021
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