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Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms.
A space-sweep approach to true multi-image matching
Robert T Collins · 1996
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Accurate, dense, and robust multiview stereopsis
Yasutaka Furukawa and Jean Ponce · 2009
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Large-scale data for multiple-view stereopsis
Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl · 2016
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Gipuma: Massively parallel multi-view stereo reconstruction
Silvano Galliani, Katrin Lasinger, and Konrad Schindler · 2016
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Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
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Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 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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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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Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
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Learning deep transformer models for machine translation
Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao · 2019
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Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Deep stereo using adaptive thin volume representation with uncertainty awareness
Shuo Cheng, Zexiang Xu, Shilin Zhu, Zhuwen Li, Li Erran Li, Ravi Ramamoorthi, and Hao Su · 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, et al · 2020
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Cascade cost volume for high-resolution multi-view stereo and stereo matching
Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan · 2020
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How much position information do convolutional neural networks encode?
Md Amirul Islam, Sen Jia, and Neil DB Bruce · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Glu variants improve transformer
Noam Shazeer · 2020
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Learning inverse depth regression for multi-view stereo with correlation cost volume
Qingshan Xu and Wenbing Tao · 2020
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Dense hybrid recurrent multi-view stereo net with dynamic consistency checking
Jianfeng Yan, Zizhuang Wei, Hongwei Yi, Mingyu Ding, Runze Zhang, Yisong Chen, Guoping Wang, and Yu-Wing Tai · 2020
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Blendedmvs: A large-scale dataset for generalized multi-view stereo networks
Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan · 2020
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Side-tuning: a baseline for network adaptation via additive side networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Malik · 2020
Cited alongside, same era.
Deep vit features as dense visual descriptors
Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel · 2021
Cited alongside, same era.
Rezero is all you need: Fast convergence at large depth
Thomas Bachlechner, Bodhisattwa Prasad Majumder, Henry Mao, Gary Cottrell, and Julian McAuley · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Cited alongside, same era.
Twins: Revisiting the design of spatial attention in vision transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 2021
Cited alongside, same era.
Wt-mvsnet: window-based transformers for multi-view stereo
Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang, Shihao Ren, Jia Guo, Wensen Feng, and Kai Zhang · 2022
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Generalized binary search network for highly-efficient multi-view stereo
Zhenxing Mi, Chang Di, and Dan Xu · 2022
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Rethinking depth estimation for multi-view stereo: A unified representation
Rui Peng, Rongjie Wang, Zhenyu Wang, Yawen Lai, and Ronggang Wang · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Quadtree attention for vision transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan · 2022
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Mvster: Epipolar transformer for efficient multi-view stereo
Xiaofeng Wang, Zheng Zhu, Guan Huang, Fangbo Qin, Yun Ye, Yijia He, Xu Chi, and Xingang Wang · 2022
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Attention is not all you need: Pure attention loses rank doubly exponentially with depth
Yihe Dong, Jean-Baptiste Cordonnier, and Andreas Loukas · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers
Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X Creighton, Russell H Taylor, and Mathias Unberath · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Efficient attention: Attention with linear complexities
Zhuoran Shen, Mingyuan Zhang, Haiyu Zhao, Shuai Yi, and Hongsheng Li · 2021
Cited alongside, same era.
Scale operation of attention from the perspective of entropy invariance, Dec 2021
Jianlin Su · 2021
Cited alongside, same era.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu · 2021
Cited alongside, same era.
Later among the works it cites.
Raymvsnet: Learning ray-based 1d implicit fields for accurate multi-view stereo
Junhua Xi, Yifei Shi, Yijie Wang, Yulan Guo, and Kai Xu · 2022
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Non-parametric depth distribution modelling based depth inference for multi-view stereo
Jiayu Yang, Jose M Alvarez, and Miaomiao Liu · 2022
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Metaformer is actually what you need for vision
Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, and Shuicheng Yan · 2022
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Riav-mvs: Recurrent-indexing an asymmetric volume for multi-view stereo
Changjiang Cai, Pan Ji, Qingan Yan, and Yi Xu · 2023
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Improving transformer-based image matching by cascaded capturing spatially informative keypoints
Chenjie Cao and Yanwei Fu · 2023
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Costformer: Cost transformer for cost aggregation in multi-view stereo
Weitao Chen, Hongbin Xu, Zhipeng Zhou, Yang Liu, Baigui Sun, Wenxiong Kang, and Xuansong Xie · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2023
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Rethinking optical flow from geometric matching consistent perspective
Qiaole Dong, Chenjie Cao, and Yanwei Fu · 2023
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Nr-mvsnet: Learning multi-view stereo based on normal consistency and depth refinement
Jingliang Li, Zhengda Lu, Yiqun Wang, Jun Xiao, and Ying Wang · 2023
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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
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Teaching matters: Investigating the role of supervision in vision transformers
Matthew Walmer, Saksham Suri, Kamal Gupta, and Abhinav Shrivastava · 2023
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Iterative geometry encoding volume for stereo matching
Gangwei Xu, Xianqi Wang, Xiaohuan Ding, and Xin Yang · 2023
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Xinyi Ye, Weiyue Zhao, Tianqi Liu, Zihao Huang, Zhiguo Cao, and Xin Li · 2023
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Biformer: Vision transformer with bi-level routing attention
Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau · 2023
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