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By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail.
High-accuracy stereo depth maps using structured light
Daniel Scharstein and Richard Szeliski · 2003
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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
Pushmeet Kohli Nathan Silberman, Derek Hoiem 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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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High-resolution stereo datasets with subpixel-accurate ground truth
Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nešić, Xi Wang, and Porter Westling · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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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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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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A multi-view stereo benchmark with high-resolution images and multi-camera videos
Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger · 2017
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Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer
Amir Atapour-Abarghouei and Toby P Breckon · 2018
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Evaluation of cnn-based single-image depth estimation methods
Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, and Marco Korner · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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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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DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2019
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Fastdepth: Fast monocular depth estimation on embedded systems
Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, and Vivienne Sze · 2019
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Yohann Cabon, Naila Murray, and Martin Humenberger · 2020
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Oasis: A large-scale dataset for single image 3d in the wild
Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, and Jia Deng · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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A survey on deep learning techniques for stereo-based depth estimation
Hamid Laga, Laurent Valentin Jospin, Farid Boussaid, and Mohammed Bennamoun · 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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Diversedepth: Affine-invariant depth prediction using diverse data
Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, and Dou Renyin · 2020
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Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2020
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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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Hierarchical integration diffusion model for realistic image deblurring
Zheng Chen, Yulun Zhang, Ding Liu, Jinjin Gu, Linghe Kong, Xin Yuan, et al · 2023
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Diffusiondepth: Diffusion denoising approach for monocular depth estimation
Yiqun Duan, Xianda Guo, and Zheng Zhu · 2023
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Ddp: Diffusion model for dense visual prediction
Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, and Ping Luo · 2023
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Latent consistency models: Synthesizing high-resolution images with few-step inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao · 2023
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Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding
Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Learning to recover 3d scene shape from a single image
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Srdiff: Single image super-resolution with diffusion probabilistic models
Haoying Li, Yifan Yang, Meng Chang, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen · 2022
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The surprising effectiveness of diffusion models for optical flow and monocular depth estimation
Saurabh Saxena, Charles Herrmann, Junhwa Hur, Abhishek Kar, Mohammad Norouzi, Deqing Sun, and David J Fleet · 2023
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Monocular depth estimation using diffusion models
Saurabh Saxena, Abhishek Kar, Mohammad Norouzi, and David J Fleet · 2023
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Metric3d: Towards zero-shot metric 3d prediction from a single image
Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen · 2023
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Unleashing text-to-image diffusion models for visual perception
Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie Zhou, and Jiwen Lu · 2023
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Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image
Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long · 2024
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Depthfm: Fast monocular depth estimation with flow matching
Ming Gui, Johannes S Fischer, Ulrich Prestel, Pingchuan Ma, Dmytro Kotovenko, Olga Grebenkova, Stefan Andreas Baumann, Vincent Tao Hu, and Björn Ommer · 2024
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Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation
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Repurposing diffusion-based image generators for monocular depth estimation
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Stereo conversion with disparity-aware warping, compositing and inpainting
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Unidepth: Universal monocular metric depth estimation
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu · 2024
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Lossy image compression with foundation diffusion models
Lucas Relic, Roberto Azevedo, Markus Gross, and Christopher Schroers · 2024
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Depth anything: Unleashing the power of large-scale unlabeled data
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Depth anything v2
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