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We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context.
Scale-space and edge detection using anisotropic diffusion
Pietro Perona and Jitendra Malik · 1990
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Robust anisotropic diffusion
Michael J Black, Guillermo Sapiro, David H Marimont, and David Heeger · 1998
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Anisotropic diffusion in image processing
Joachim Weickert · 1998
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Geometric context from a single image
Derek Hoiem, Alexei A. Efros, and Martial Hebert · 2005
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Bilateral filtering-based optical flow estimation with occlusion detection
Jiangjian Xiao, Hui Cheng, Harpreet Sawhney, Cen Rao, and Michael Isnardi · 2006
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Local grouping for optical flow
Xiaofeng Ren · 2008
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Make3d: Learning 3d scene structure from a single still image
Ashutosh Saxena, Min Sun, and Andrew Y Ng · 2008
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Nonrigid structure-from-motion: Estimating shape and motion with hierarchical priors
Lorenzo Torresani, Aaron Hertzmann, and Chris Bregler · 2008
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Depth extraction from video using non-parametric sampling
Kevin Karsch, Ce Liu, and Sing Bing Kang · 2012
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Data-driven 3d primitives for single image understanding
David F Fouhey, Abhinav Gupta, and Martial Hebert · 2013
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Fast edge-preserving patchmatch for large displacement optical flow
Linchao Bao, Qingxiong Yang, and Hailin Jin · 2014
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Unfolding an indoor origami world
David Ford Fouhey, Abhinav Gupta, and Martial Hebert · 2014
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Local stereo matching with improved matching cost and disparity refinement
Jianbo Jiao, Ronggang Wang, Wenmin Wang, Shengfu Dong, Zhenyu Wang, and Wen Gao · 2014
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Pulling things out of perspective
Lubor Ladicky, Jianbo Shi, and Marc Pollefeys · 2014
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Discrete-continuous depth estimation from a single image
Miaomiao Liu, Mathieu Salzmann, and Xuming He · 2014
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Includes all Grand Theft Auto online and Grand Theft Auto V free-to-access content updates
Grand Theft Auto V, [2014] · 2014
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Discriminatively trained dense surface normal estimation
L’ubor Ladickỳ, Bernhard Zeisl, and Marca Pollefeys · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
Bo Li, Chunhua Shen, Yuchao Dai, Anton Van Den Hengel, and Mingyi He · 2015
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Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian Reid · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, and Cordelia Schmid · 2015
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Towards unified depth and semantic prediction from a single image
Peng Wang, Xiaohui Shen, Zhe Lin, Scott Cohen, Brian Price, and Alan L Yuille · 2015
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Designing deep networks for surface normal estimation
Xiaolong Wang, David Fouhey, and Abhinav Gupta · 2015
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Depth from a single image by harmonizing overcomplete local network predictions
Ayan Chakrabarti, Jingyu Shao, and Greg Shakhnarovich · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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
Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries
Junjie Hu, Mete Ozay, Yan Zhang, and Takayuki Okatani · 2019
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From big to small: Multi-scale local planar guidance for monocular depth estimation
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
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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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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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Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
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Dense monocular depth estimation in complex dynamic scenes
Rene Ranftl, Vibhav Vineet, Qifeng Chen, and Vladlen Koltun · 2016
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Monocular depth estimation using neural regression forest
Anirban Roy and Sinisa Todorovic · 2016
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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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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
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A two-streamed network for estimating fine-scaled depth maps from single rgb images
Jun Li, Reinhard Klein, and Angela Yao · 2017
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Multi-scale continuous crfs as sequential deep networks for monocular depth estimation
Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, and Nicu Sebe · 2017
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Pattern-affinitive propagation across depth, surface normal and semantic segmentation
Zhenyu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, and Jian Yang · 2019
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Normal assisted stereo depth estimation
Uday Kusupati, Shuo Cheng, Rui Chen, and Hao Su · 2020
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Occlusion-aware depth estimation with adaptive normal constraints
Xiaoxiao Long, Lingjie Liu, Christian Theobalt, and Wenping Wang · 2020
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Geonet++: Iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H. S. Torr, Raquel Urtasun, and Jiaya Jia · 2020
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Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al · 2020
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Variational level set evolution for non-rigid 3D reconstruction from a single depth camera
Miroslava Slavcheva, Maximilian Baust, and Slobodan Ilic · 2020
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Learning steering kernels for guided depth completion
Lina Liu, Yiyi Liao, Yue Wang, Andreas Geiger, and Yong Liu · 2021
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Fcfr-net: Feature fusion based coarse-to-fine residual learning for depth completion
Lina Liu, Xibin Song, Xiaoyang Lyu, Junwei Diao, Mengmeng Wang, Yong Liu, and Liangjun Zhang · 2021
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Adaptive surface normal constraint for depth estimation
Xiaoxiao Long, Cheng Lin, Lingjie Liu, Wei Li, Christian Theobalt, Ruigang Yang, and Wenping Wang · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras
Zachary Teed and Jia Deng · 2021
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Volumefusion: Deep depth fusion for 3d scene reconstruction
Jaesung Choe, Sunghoon Im, Francois Rameau, Minjun Kang, and In So Kweon · 2021
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Depth-Guided Sparse Structure-from-Motion for Movies and TV Shows
Sheng Liu, Xiaohan Nie, and Raffay Hamid · 2022
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Gen6D: Generalizable model-free 6-DoF object pose estimation from RGB images
Yuan Liu, Yilin Wen, Sida Peng, Cheng Lin, Xiaoxiao Long, Taku Komura and Wang, Wenping · 2022
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Cerberus transformer: Joint semantic, affordance and attribute parsing
Xiaoxue Chen, Tianyu Liu, Hao Zhao, Guyue Zhou, and Ya-Qin Zhang · 2022
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Steps: Joint self-supervised nighttime image enhancement and depth estimation
Yupeng Zheng, Chengliang Zhong, Pengfei Li, Huan-ang Gao, Yuhang Zheng, Bu Jin, Ling Wang, Hao Zhao, Guyue Zhou, Qichao Zhang, and others · 2023
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DPF: Learning Dense Prediction Fields with Weak Supervision
Xiaoxue Chen, Yuhang Zheng, Yupeng Zheng, Qiang Zhou, Hao Zhao, Guyue Zhou, and Ya-Qin Zhang · 2023
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