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Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame.
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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Multi-view stereo: A tutorial
Yasutaka Furukawa, Carlos Hernández, et al · 2015
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Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
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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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MegaDepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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MVSNet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
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Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera
Yuhua Chen, Cordelia Schmid, and Cristian Sminchisescu · 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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ReFusion: 3d reconstruction in dynamic environments for RGB-D cameras exploiting residuals
Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguere, and Cyrill Stachniss · 2019
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Exploiting temporal consistency for real-time video depth estimation
Haokui Zhang, Chunhua Shen, Ying Li, Yuanzhouhan Cao, Yu Liu, and Youliang Yan · 2019
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3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Consistent video depth estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf · 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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Deepv2d: Video to depth with differentiable structure from motion
Zachary Teed and Jia Deng · 2020
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TartanAir: A dataset to push the limits of visual SLAM
Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer · 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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Bidirectional attention network for monocular depth estimation
Shubhra Aich, Jean Marie Uwabeza Vianney, Md Amirul Islam, Mannat Kaur, and Bingbing Liu · 2021
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AdaBins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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AliceVision Meshroom: An open-source 3d reconstruction pipeline
Carsten Griwodz, Simone Gasparini, Lilian Calvet, Pierre Gurdjos, Fabien Castan, Benoit Maujean, Gregoire De Lillo, and Yann Lanthony · 2021
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Robust consistent video depth estimation
Johannes Kopf, Xuejian Rong, and Jia-Bin Huang · 2021
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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 · 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
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Transformer-based attention networks for continuous pixel-wise prediction
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, and Elisa Ricci · 2021
Cited alongside, same era.
Consistent depth of moving objects in video
Zhoutong Zhang, Forrester Cole, Richard Tucker, William T Freeman, and Tali Dekel · 2021
Cited alongside, same era.
BinsFormer: Revisiting adaptive bins for monocular depth estimation
Zhenyu Li, Xuyang Wang, Xianming Liu, and Junjun Jiang · 2022
Cited alongside, same era.
Monocular depth estimation using diffusion models
Saurabh Saxena, Abhishek Kar, Mohammad Norouzi, and David J Fleet · 2023
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Neural video depth stabilizer
Yiran Wang, Min Shi, Jiaqi Li, Zihao Huang, Zhiguo Cao, Jianming Zhang, Ke Xian, and Guosheng Lin · 2023
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Tune-A-Video: One-shot tuning of image diffusion models for text-to-video generation
Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou · 2023
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Rerender a video: Zero-shot text-guided video-to-video translation
Shuai Yang, Yifan Zhou, Ziwei Liu, and Chen Change Loy · 2023
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MAMo: Leveraging memory and attention for monocular video depth estimation
Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi, Risheek Garrepalli, and Fatih Porikli · 2023
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Vaishakh Patil, Christos Sakaridis, Alexander Liniger, and Luc Van Gool · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Cited alongside, same era.
Consistent direct time-of-flight video depth super-resolution
Zhanghao Sun, Wei Ye, Jinhui Xiong, Gyeongmin Choe, Jialiang Wang, Shuochen Su, and Rakesh Ranjan · 2022
Cited alongside, same era.
Less is more: Consistent video depth estimation with masked frames modeling
Yiran Wang, Zhiyu Pan, Xingyi Li, Zhiguo Cao, Ke Xian, and Jianming Zhang · 2022
Cited alongside, same era.
GMFlow: Learning optical flow via global matching
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao · 2022
Cited alongside, same era.
NeWCRFs: Neural window fully-connected CRFs for monocular depth estimation
Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan · 2022
Cited alongside, same era.
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
Later among the works it cites.
PointOdyssey: A large-scale synthetic dataset for long-term point tracking
Yang Zheng, Adam W Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J Guibas · 2023
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Depth Pro: Sharp monocular metric depth in less than a second
Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R Richter, and Vladlen Koltun · 2024
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Warped diffusion: Solving video inverse problems with image diffusion models
Giannis Daras, Weili Nie, Karsten Kreis, Alex Dimakis, Morteza Mardani, Nikola Borislavov Kovachki, and Arash Vahdat · 2024
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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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Fine-tuning image-conditional diffusion models is easier than you think
Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe · 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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Lotus: Diffusion-based visual foundation model for high-quality dense prediction
Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Liu, Bingbing Liu, and Ying-Cong Chen · 2024
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Repurposing diffusion-based image generators for monocular depth estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler · 2024
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EscherNet: A generative model for scalable view synthesis
Xin Kong, Shikun Liu, Xiaoyang Lyu, Marwan Taher, Xiaojuan Qi, and Andrew J Davison · 2024
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Solving video inverse problems using image diffusion models
Taesung Kwon and Jong Chul Ye · 2024
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Common diffusion noise schedules and sample steps are flawed
Shanchuan Lin, Bingchen Liu, Jiashi Li, and Xiao Yang · 2024
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Video-P2P: Video editing with cross-attention control
Shaoteng Liu, Yuechen Zhang, Wenbo Li, Zhe Lin, and Jiaya Jia · 2024
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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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Learning temporally consistent video depth from video diffusion priors
Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Matteo Poggi, and Yiyi Liao · 2024
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Diffusion models trained with large data are transferable visual models
Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan, Kangyang Xie, Zhiyue Zhao, Hao Chen, and Chunhua Shen · 2024
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FutureDepth: Learning to predict the future improves video depth estimation
Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, and Fatih Porikli · 2024
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UniDepthV2: Universal monocular metric depth estimation made simpler
Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang, Mattia Segu, Siyuan Li, Wim Abbeloos, and Luc Van Gool · 2025
Closest in time.