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In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image.
Learning depth from single monocular images
Ashutosh Saxena, Sung Chung, and Andrew Ng · 2006
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Ashutosh Saxena, Min Sun, and Andrew Ng · 2009
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A survey of human motion analysis using depth imagery
Lulu Chen, Hong Wei, and James Ferryman · 2013
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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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Panocontext: A whole-room 3d context model for panoramic scene understanding
Yinda Zhang, Shuran Song, Ping Tan, and Jianxiong Xiao · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
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Fayao Liu, Chunhua Shen, and Guosheng Lin · 2015
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Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo · 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 Yuille · 2015
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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
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The adaptive berhu penalty in robust regression
Sophie Lambert-Lacroix and Laurent Zwald · 2016
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Dense monocular depth estimation in complex dynamic scenes
René Ranftl, Vibhav Vineet, Qifeng Chen, and Vladlen Koltun · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Simultaneous localization and mapping: A survey of current trends in autonomous driving
Guillaume Bresson, Zayed Alsayed, Li Yu, and Sébastien Glaser · 2017
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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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Depth estimation using structured light flow — analysis of projected pattern flow on an object’s surface
Ryo Furukawa, Ryusuke Sagawa, and Hiroshi Kawasaki · 2017
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Unsupervised monocular depth estimation with left-right consistency
Clement Godard, Oisin Mac Aodha, and Gabriel J. Brostow · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 2017
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Semantic scene completion from a single depth image
Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser · 2017
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Learning spherical convolution for fast features from 360°imagery
Yu-Chuan Su and Kristen Grauman · 2017
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Cnn-slam: Real-time dense monocular slam with learned depth prediction
Keisuke Tateno, Federico Tombari, Iro Laina, and Nassir Navab · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Brown Brown, Noah Snavely, and David G. Lowe · 2017
Pano popups: Indoor 3d reconstruction with a plane-aware network
Marc Eder, Pierre Moulon, and Li Guan · 2019
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A survey on deep learning architectures for image-based depth reconstruction
Hamid Laga · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Kernel transformer networks for compact spherical convolution
Yu-Chuan Su and Kristen Grauman · 2019
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360sd-net: 360
Ning-Hsu Wang, Bolivar Solarte, Yi-Hsuan Tsai, Wei-Chen Chiu, and Min Sun · 2019
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Yuanzhouhan Cao, Zifeng Wu, and Chunhua Shen · 2018
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Cube padding for weakly-supervised saliency prediction in 360
Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai Wen, Tyng-Luh Liu, and Min Sun · 2018
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Spherical CNNs
Taco S. Cohen, Mario Geiger, Jonas Köhler, and Max Welling · 2018
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Spherenet: Learning spherical representations for detection and classification in omnidirectional images
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Structured attention guided convolutional neural fields for monocular depth estimation
Xu Dan, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, and Elisa Ricci · 2018
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Single-image depth estimation based on fourier domain analysi
Jae-Han Lee, Minhyeok Heo, Kyung-Rae Kim, and Chang-Su Kim · 2018
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Spherical view synthesis for self-supervised 360
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Deep depth estimation on 360
Brandon Yushan Feng, Wangjue Yao, Zheyuan Liu, and Amitabh Varshney · 2020
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Geometric structure based and regularized depth estimation from 360 indoor imagery
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Deep learning-based monocular depth estimation methods—a state-of-the-art review
Faisal Khan, Saqib Salahuddin, and Hossein Javidnia · 2020
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Fisheyedistancenet: Self-supervised scale-aware distance estimation using monocular fisheye camera for autonomous driving
Varun Ravi Kumar, Sandesh Athni Hiremath, Markus Bach, Stefan Milz, Christian Witt, Clément Pinard, Senthil Yogamani, and Patrick Mäder · 2020
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State-of-the-art in automatic 3d reconstruction of structured indoor environments
Giovanni Pintore, Claudio Mura, Fabio Ganovelli, Lizeth Fuentes-Perez, Renato Pajarola, and Enrico Gobbetti · 2020
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Bifuse: Monocular 360 depth estimation via bi-projection fusion
Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, and Yi-Hsuan Tsai · 2020
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Joint 3d layout and depth prediction from a single indoor panorama image
Wei Zeng, Sezer Karaoglu, and Theo Gevers · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Unifuse: Unidirectional fusion for 360° panorama depth estimation
Hualie Jiang, Zhe Sheng, Siyu Zhu, Zilong Dong, and Rui Huang · 2021
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Deep learning for monocular depth estimation: A review
Yue Ming, Xuyang Meng, Chunxiao Fan, and Hui Yu · 2021
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Slicenet: Deep dense depth estimation from a single indoor panorama using a slice-based representation
Giovanni Pintore, Marco Agus, Eva Almansa, Jens Schneider, and Enrico Gobbetti · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Hohonet: 360 indoor holistic understanding with latent horizontal features
Cheng Sun, Min Sun, and Hwann-Tzong Chen · 2021
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