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Depth estimation from monocular images is pivotal for real-world visual perception systems.
Make3d: Learning 3d scene structure from a single still image
Ashutosh Saxena, Min Sun, and Andrew Y. Ng · 2008
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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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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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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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, BG Vijay Kumar, Gustavo Carneiro, and Ian Reid · 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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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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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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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G. Lowe · 2017
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Semantic foggy scene understanding with synthetic data
Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Monocular relative depth perception with web stereo data supervision
Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Yang Xiao, Ruibo Li, and Zhenbo Luo · 2018
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Learning single-image depth from videos using quality assessment networks
Weifeng Chen, Shengyi Qian, and Jia Deng · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
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Digging into self-supervised monocular depth prediction
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 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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Deep attention-based classification network for robust depth prediction
Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, and Lingxiao Hang · 2019
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel · 2019
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Improving self-supervised single view depth estimation by masking occlusion
Maarten Schellevis · 2019
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Learning monocular depth estimation infusing traditional stereo knowledge
Fabio Tosi, Filippo Aleotti, Matteo Poggi, and Stefano Mattoccia · 2019
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Web stereo video supervision for depth prediction from dynamic scenes
Chaoyang Wang, Simon Lucey, Federico Perazzi, and Oliver Wang · 2019
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Self-supervised monocular depth hints
Jamie Watson, Michael Firman, Gabriel J. Brostow, and Daniyar Turmukhambetov · 2019
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume
Adrian Johnston and Gustavo Carneiro · 2020
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Benchmarking the robustness of semantic segmentation models
Christoph Kamann and Carsten Rother · 2020
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A survey on deep learning techniques for stereo-based depth estimation
Image masking for robust self-supervised monocular depth estimation
Hemang Chawla, Kishaan Jeeveswaran, Elahe Arani, and Bahram Zonooz · 2022
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Physical attack on monocular depth estimation with optimal adversarial patches
Zhiyuan Cheng, James Liang, Hongjun Choi, Guanhong Tao, Zhiwen Cao, Dongfang Liu, and Xiangyu Zhang · 2022
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Towards real-time monocular depth estimation for robotics: A survey
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti, and Hussein A. Abbass · 2022
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Event-based vision: A survey
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza · 2022
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Seasondepth: Cross-season monocular depth prediction dataset and benchmark under multiple environments
Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Shiqi Liu, Ding Zhao, and Hesheng Wang · 2022
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Hamid Laga, Laurent Valentin Jospin, Farid Boussaid, and Mohammed Bennamoun · 2020
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Sdc-depth: Semantic divide-and-conquer network for monocular depth estimation
Lijun Wang, Jianming Zhang, Oliver Wang, Zhe Lin, and Huchuan Lu · 2020
Cited alongside, same era.
Toward hierarchical self-supervised monocular absolute depth estimation for autonomous driving applications
Feng Xue, Guirong Zhuo, Ziyuan Huang, Wufei Fu, Zhuoyue Wu, and Marcelo H. Ang · 2020
Cited alongside, same era.
Monocular depth estimation based on deep learning: An overview
Chaoqiang Zhao, Qiyu Sun, Chongzhen Zhang, Yang Tang, and Feng Qian · 2020
Cited alongside, same era.
Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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Robustnav: Towards benchmarking robustness in embodied navigation
Prithvijit Chattopadhyay, Judy Hoffman, Roozbeh Mottaghi, and Aniruddha Kembhavi · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
Cited alongside, same era.
3d common corruptions and data augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir · 2022
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Simipu: Simple 2d image and 3d point cloud unsupervised pre-training for spatial-aware visual representations
Zhenyu Li, Zehui Chen, Ang Li, Liangji Fang, Qinhong Jiang, Xianming Liu, Junjun Jiang, Bolei Zhou, and Hang Zhao · 2022
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Zhenyu Li, Zehui Chen, Xianming Liu, and Junjun Jiang · 2022
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Ra-depth: Resolution adaptive self-supervised monocular depth estimation
He Mu, Hui Le, Bian Yikai, Ren Jian, Xie Jin, and Yang Jian · 2022
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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 · 2022
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Sc-depthv3: Robust self-supervised monocular depth estimation for dynamic scenes
Libo Sun, Jia-Wang Bian, Huangying Zhan, Wei Yin, Ian Reid, and Chunhua Shen · 2022
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Hierarchical normalization for robust monocular depth estimation
Chi Zhang, Wei Yin, Billzb Wang, Gang Yu, Bin Fu, and Chunhua Shen · 2022
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Towards scale-aware, robust, and generalizable unsupervised monocular depth estimation by integrating imu motion dynamics
Sen Zhang, Jing Zhang, and Dacheng Tao · 2022
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Monovit: Self-supervised monocular depth estimation with a vision transformer
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi, Fabio Tosi, Xianda Guo, Zheng Zhu, Guan Huang, Yang Tang, and Stefano Mattoccia · 2022
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Self-supervised monocular depth estimation: Solving the edge-fattening problem
Xingyu Chen, Ruonan Zhang, Ji Jiang, Yan Wang, Ge Li, and Thomas H Li · 2023
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Rethinking range view representation for lidar segmentation
Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Ziwei Liu · 2023
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Robo3d: Towards robust and reliable 3d perception against corruptions
Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, and Ziwei Liu · 2023
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The robodepth challenge: Methods and advancements towards robust depth estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit Cottereau, Ding Zhao, Liangjun Zhang, Hesheng Wang, Wei Tsang Ooi, Ruijie Zhu, Ziyang Song, Li Liu, Tianzhu Zhang, Jun Yu, Mohan Jing, Pengwei Li, Xiaohua Qi, Cheng Jin, Yingfeng Chen, Jie Hou, Jie Zhang, Zhen Kan, Qiang Lin, Liang Peng, Minglei Li, Di Xu, Changpeng Yang, Yuanqi Yao, Gang Wu, Jian Kuai, Xianming Liu, Junjun Jiang, Jiamian Huang, Baojun Li, Jiale Chen, Shuang Zhang, Sun Ao, Zhenyu Li, Runze Chen, Haiyong Luo, Fang Zhao, and Jingze Yu · 2023
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Lasermix for semi-supervised lidar semantic segmentation
Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu · 2023
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Uniseg: A unified multi-modal lidar segmentation network and the openpcseg codebase
Youquan Liu, Runnan Chen, Xin Li, Lingdong Kong, Yuchen Yang, Zhaoyang Xia, Yeqi Bai, Xinge Zhu, Yuexin Ma, Yikang Li, Yu Qiao, and Yuenan Hou · 2023
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Segment any point cloud sequences by distilling vision foundation models
Youquan Liu, Lingdong Kong, Jun Cen, Runnan Chen, Wenwei Zhang, Liang Pan, Kai Chen, and Ziwei Liu · 2023
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Lite-mono: A lightweight cnn and transformer architecture for self-supervised monocular depth estimation
Ning Zhang, Francesco Nex, George Vosselman, and Norman Kerle · 2023
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