Fetching the paper…
Reading the bibliography…
In this work, we explore new perspectives on cross-view completion learning by drawing an analogy to self-supervised correspondence learning.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Earlier work this paper cites.
Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
Jason J Yu, Adam W Harley, and Konstantinos G Derpanis · 2016
Earlier work this paper cites.
Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
Earlier work this paper cites.
Single-view and multi-view depth fusion
José M Fácil, Alejo Concha, Luis Montesano, and Javier Civera · 2017
Earlier work this paper cites.
Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Unsupervised deep learning for optical flow estimation
Zhe Ren, Junchi Yan, Bingbing Ni, Bin Liu, Xiaokang Yang, and Hongyuan Zha · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Attention is all you need
A Vaswani · 2017
Earlier work this paper cites.
Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
Earlier work this paper cites.
Recurrent transformer networks for semantic correspondence
Seungryong Kim, Stephen Lin, Sang Ryul Jeon, Dongbo Min, and Kwanghoon Sohn · 2018
Earlier work this paper cites.
Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
Earlier work this paper cites.
Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Simon Meister, Junhwa Hur, and Stefan Roth · 2018
Earlier work this paper cites.
Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
Earlier work this paper cites.
Unsupervised monocular depth and ego-motion learning with structure and semantics
Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova · 2019
Earlier work this paper cites.
Digging into self-supervised monocular depth estimation
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow · 2019
Earlier work this paper cites.
Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova · 2019
Earlier work this paper cites.
Sfnet: Learning object-aware semantic correspondence
Junghyup Lee, Dohyung Kim, Jean Ponce, and Bumsub Ham · 2019
Earlier work this paper cites.
Selflow: Self-supervised learning of optical flow
Pengpeng Liu, Michael Lyu, Irwin King, and Jia Xu · 2019
Earlier work this paper cites.
Dgc-net: Dense geometric correspondence network
Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, and Juho Kannala · 2019
Earlier work this paper cites.
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, et al · 2019
Earlier work this paper cites.
Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos
Yang Wang, Peng Wang, Zhenheng Yang, Chenxu Luo, Yi Yang, and Wei Xu · 2019
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume
Adrian Johnston and Gustavo Carneiro · 2020
Cited alongside, same era.
Dualrefine: Self-supervised depth and pose estimation through iterative epipolar sampling and refinement toward equilibrium
Antyanta Bangunharcana, Ahmed Magd, and Kyung-Soo Kim · 2023
Later among the works it cites.
Vision transformers need registers
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski · 2023
Later among the works it cites.
Dkm: Dense kernelized feature matching for geometry estimation
Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck, and Michael Felsberg · 2023
Later among the works it cites.
Siamese masked autoencoders
Agrim Gupta, Jiajun Wu, Jia Deng, and Fei-Fei Li · 2023
Later among the works it cites.
Robodepth: Robust out-of-distribution depth estimation under corruptions
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, and Wei Tsang Ooi · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
What matters in unsupervised optical flow, 2020
Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova · 2020
Cited alongside, same era.
Don’t forget the past: Recurrent depth estimation from monocular video
Vaishakh Patil, Wouter Van Gansbeke, Dengxin Dai, and Luc Van Gool · 2020
Cited alongside, same era.
Ransac-flow: generic two-stage image alignment
Xi Shen, François Darmon, Alexei A Efros, and Mathieu Aubry · 2020
Cited alongside, same era.
Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
Cited alongside, same era.
Cats: Cost aggregation transformers for visual correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, and Seungryong Kim · 2021
Cited alongside, same era.
Geometric unsupervised domain adaptation for semantic segmentation
Vitor Guizilini, Jie Li, Rareș Ambruș, and Adrien Gaidon · 2021
Cited alongside, same era.
Deep matching prior: Test-time optimization for dense correspondence
Sunghwan Hong and Seungryong Kim · 2021
Cited alongside, same era.
Learning to fuse monocular and multi-view cues for multi-frame depth estimation in dynamic scenes
Rui Li, Dong Gong, Wei Yin, Hao Chen, Yu Zhu, Kaixuan Wang, Xiaozhi Chen, Jinqiu Sun, and Yanning Zhang · 2023
Later among the works it cites.
Diffusion model for dense matching
Jisu Nam, Gyuseong Lee, Sunwoo Kim, Hyeonsu Kim, Hyoungwon Cho, Seyeon Kim, and Seungryong Kim · 2023
Later among the works it cites.
Dinov2: Learning robust visual features without supervision, 2023
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
Later among the works it cites.
Emergent correspondence from image diffusion
Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan · 2023
Later among the works it cites.
Pdc-net+: Enhanced probabilistic dense correspondence network
Prune Truong, Martin Danelljan, Radu Timofte, and Luc Van Gool · 2023
Later among the works it cites.
Crafting monocular cues and velocity guidance for self-supervised multi-frame depth learning
Xiaofeng Wang, Zheng Zhu, Guan Huang, Xu Chi, Yun Ye, Ziwei Chen, and Xingang Wang · 2023
Later among the works it cites.
Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow
Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud · 2023
Later among the works it cites.
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.
Adaptive fusion of single-view and multi-view depth for autonomous driving
Junda Cheng, Wei Yin, Kaixuan Wang, Xiaozhi Chen, Shijie Wang, and Xin Yang · 2024
Closest in time.
Cat-seg: Cost aggregation for open-vocabulary semantic segmentation
Seokju Cho, Heeseong Shin, Sunghwan Hong, Anurag Arnab, Paul Hongsuck Seo, and Seungryong Kim · 2024
Closest in time.
Roma: Robust dense feature matching
Johan Edstedt, Qiyu Sun, Georg Bökman, Mårten Wadenbäck, and Michael Felsberg · 2024
Closest in time.
Probing the 3d awareness of visual foundation models
Mohamed El Banani, Amit Raj, Kevis-Kokitsi Maninis, Abhishek Kar, Yuanzhen Li, Michael Rubinstein, Deqing Sun, Leonidas Guibas, Justin Johnson, and Varun Jampani · 2024
Closest in time.
Few-shot object detection with foundation models
Guangxing Han and Ser-Nam Lim · 2024
Closest in time.
Unifying feature and cost aggregation with transformers for semantic and visual correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim, and Stephen Lin · 2024
Closest in time.
Repurposing diffusion-based image generators for monocular depth estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler · 2024
Closest in time.
Grounding image matching in 3d with mast3r
Vincent Leroy, Yohann Cabon, and Jérôme Revaud · 2024
Closest in time.
Flowdiffuser: Advancing optical flow estimation with diffusion models
Ao Luo, Xin Li, Fan Yang, Jiangyu Liu, Haoqiang Fan, and Shuaicheng Liu · 2024
Closest in time.
Unidepth: Universal monocular metric depth estimation
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, and Fisher Yu · 2024
Closest in time.
Movie gen: A cast of media foundation models
Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih-Yao Ma, Ching-Yao Chuang, et al · 2024
Closest in time.
Sacreg: Scene-agnostic coordinate regression for visual localization
Jerome Revaud, Yohann Cabon, Romain Brégier, JongMin Lee, and Philippe Weinzaepfel · 2024
Closest in time.
Dust3r: Geometric 3d vision made easy
Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud · 2024
Closest in time.
Depth anything: Unleashing the power of large-scale unlabeled data
Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao · 2024
Closest in time.
A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa Polania Cabrera, Varun Jampani, Deqing Sun, and Ming-Hsuan Yang · 2024
Closest in time.
A survey on open-vocabulary detection and segmentation: Past, present, and future
Chaoyang Zhu and Long Chen · 2024
Closest in time.
Sea-raft: Simple, efficient, accurate raft for optical flow
Yihan Wang, Lahav Lipson, and Jia Deng · 2025
Closest in time.