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Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for.
Tossingbot: Learning to throw arbitrary objects with residual physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 1903
Earlier work this paper cites.
Finding glass
K. McHenry, J. Ponce, and D. Forsyth · 2005
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A geodesic active contour framework for finding glass
Kenton McHenry and Jean Ponce · 2006
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An additive latent feature model for transparent object recognition
Mario Fritz, Gary Bradski, Sergey Karayev, Trevor Darrell, and Michael J Black · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Transparent object detection and reconstruction on a mobile platform
Ulrich Klank, Daniel Carton, and Michael Beetz · 2011
Earlier work this paper cites.
A novel stereoscopic cue for figure-ground segregation of semi-transparent objects
Cody J Phillips, Konstantinos G Derpanis, and Kostas Daniilidis · 2011
Earlier work this paper cites.
Glass object localization by joint inference of boundary and depth
Tao Wang, Xuming He, and Nick Barnes · 2012
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Seeing the unseen: Simple reconstruction of transparent objects from point cloud data
Sven Albrecht and Stephen Marsland · 2013
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Xiaojin Gong, Junyi Liu, Wenhui Zhou, and Jilin Liu · 2013
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Ilya Lysenkov, Victor Eruhimov, and Gary Bradski · 2013
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David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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A fixed viewpoint approach for dense reconstruction of transparent objects
Kai Han, Kwan-Yee K Wong, and Miaomiao Liu · 2015
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Transparent object detection using regions with convolutional neural network
Po-Jen Lai and Chiou-Shann Fuh · 2015
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X. Wang, D. F. Fouhey, and A. Gupta · 2015
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Aayush Bansal, Bryan Russell, and Abhinav Gupta · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Deep normal estimation for automatic shading of hand-drawn characters
Matis Hudon, Mairead Grogan, Rafael Pages, and Aljosa Smolic · 2018
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Transparent object detection using convolutional neural network
May Phyo Khaing and Mukunoki Masayuki · 2018
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Soccer on your tabletop
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Cody J Phillips, Matthieu Lecce, and Kostas Daniilidis · 2016
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3d reconstruction of transparent objects with position-normal consistency
Yiming Qian, Minglun Gong, and Yee Hong Yang · 2016
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Hand3d: Hand pose estimation using 3d neural network
Xiaoming Deng, Shuo Yang, Yinda Zhang, Ping Tan, Liang Chang, and Hongan Wang · 2017
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Fusing depth and silhouette for scanning transparent object with RGB-D sensor
Yijun Ji, Qing Xia, and Zhijiang Zhang · 2017
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Face normals “in-the-wild” using fully convolutional networks
I Kokkinos, S Zafeiriou, et al · 2017
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Seongjong Song and Hyunjung Shim · 2018
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Using geometry to detect grasp poses in 3d point clouds
Andreas ten Pas and Robert Platt · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
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Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
Andy Zeng, Shuran Song, Kuan-Ting Yu, Elliott Donlon, Francois R Hogan, Maria Bauza, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, et al · 2018
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Deep depth completion of a single rgb-d image
Yinda Zhang and Thomas Funkhouser · 2018
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Transparent object detection and location based on RGB-D camera
Chen Guo-Hua, Wang Jun-Yi, and Zhang Ai-Jun · 2019
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Pytorch re-implementation of deeplabv3+, 2018
Jianfeng Zhang · 2019
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