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Majority of the perception methods in robotics require depth information provided by RGB-D cameras.
Ray tracing complex scenes
Timothy L Kay and James T Kajiya · 1986
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Transparent object detection and reconstruction on a mobile platform
Ulrich Klank, Daniel Carton, and Michael Beetz · 2011
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Matting and compositing of transparent and refractive objects
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Modeling kinect sensor noise for improved 3d reconstruction and tracking
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Indoor segmentation and support inference from rgbd images
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Intrinsic scene properties from a single rgb-d image
Jonathan T Barron and Jitendra Malik · 2013
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Recognition and pose estimation of rigid transparent objects with a kinect sensor
Ilya Lysenkov, Victor Eruhimov, and Gary Bradski · 2013
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Pose estimation of rigid transparent objects in transparent clutter
Ilya Lysenkov and Vincent Rabaud · 2013
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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Depth map prediction from a single image using a multi-scale deep network
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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, Jianxiong Xiao, Li Yi, and Fisher Yu · 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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Depth image enhancement using local tangent plane approximations
Kiyoshi Matsuo and Yoshimitsu Aoki · 2015
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Human pose estimation with iterative error feedback
Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, and Jitendra Malik · 2016
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Single-image depth perception in the wild
Weifeng Chen, Zhao Fu, Dawei Yang, and Jia Deng · 2016
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Structured prediction of unobserved voxels from a single depth image
Michael Firman, Oisin Mac Aodha, Simon Julier, and Gabriel J Brostow · 2016
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar Bg, Gustavo Carneiro, and Ian Reid · 2016
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Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
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Seeing glassware: from edge detection to pose estimation and shape recovery
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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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image
Jiaxiong Qiu, Zhaopeng Cui, Yinda Zhang, Xingdi Zhang, Shuaicheng Liu, Bing Zeng, and Marc Pollefeys · 2019
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Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li · 2019
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Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
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Depth completion from sparse lidar data with depth-normal constraints
Yan Xu, Xinge Zhu, Jianping Shi, Guofeng Zhang, Hujun Bao, and Hongsheng Li · 2019
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Deep local shapes: Learning local sdf priors for detailed 3D reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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Sparsity invariant cnns
Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger · 2017
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Depth estimation via affinity learned with convolutional spatial propagation network
Xinjing Cheng, Peng Wang, and Ruigang Yang · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Sparse-to-dense: Depth prediction from sparse depth samples and a single image
Fangchang Ma and Sertac Karaman · 2018
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Full 3d reconstruction of transparent objects
Bojian Wu, Yang Zhou, Yiming Qian, Minglun Gong, and Hui Huang · 2018
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Pcn: Point completion network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert · 2018
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Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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Local implicit grid representations for 3d scenes
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Deep polarization cues for transparent object segmentation
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Through the looking glass: Neural 3d reconstruction of transparent shapes
Zhengqin Li, Yu-Ying Yeh, and Manmohan Chandraker · 2020
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Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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Keypose: Multi-view 3d labeling and keypoint estimation for transparent objects
Xingyu Liu, Rico Jonschkowski, Anelia Angelova, and Kurt Konolige · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Non-local spatial propagation network for depth completion
Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
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Clear grasp: 3d shape estimation of transparent objects for manipulation
Shreeyak Sajjan, Matthew Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, and Shuran Song · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
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Learning rgb-d feature embeddings for unseen object instance segmentation
Yu Xiang, Christopher Xie, Arsalan Mousavian, and Dieter Fox · 2020
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