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In deep multi-view stereo networks, cost regularization is crucial to achieve accurate depth estimation.
A space-sweep approach to true multi-image matching
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Multi-camera Scene Reconstruction via Graph Cuts
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Real-Time Plane-Sweeping Stereo with Multiple Sweeping Directions
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PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing
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Accurate, Dense, and Robust Multiview Stereopsis
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Efficient large-scale multi-view stereo for ultra high-resolution image sets
Tola, E.; Strecha, C.; and Fua, P. 2012 · 2012
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Fast Cost-Volume Filtering for Visual Correspondence and Beyond
Hosni, A.; Rhemann, C.; Bleyer, M.; Rother, C.; and Gelautz, M. 2013 · 2013
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Screened Poisson Surface Reconstruction
Kazhdan, M.; and Hoppe, H. 2013 · 2013
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PatchMatch Based Joint View Selection and Depthmap Estimation
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Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
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Adam: A method for stochastic optimization
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Large-Scale Data for Multiple-View Stereopsis
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Pixelwise View Selection for Unstructured Multi-View Stereo
Schönberger, J. L.; Zheng, E.; Frahm, J.-M.; and Pollefeys, M. 2016 · 2016
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Learned Multi-patch Similarity
Hartmann, W.; Galliani, S.; Havlena, M.; Gool, L. V.; and Schindler, K. 2017 · 2017
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Densely connected convolutional networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
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Surfacenet: An end-to-end 3d neural network for multiview stereopsis
Ji, M.; Gall, J.; Zheng, H.; Liu, Y.; and Fang, L. 2017 · 2017
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Learning a Multi-View Stereo Machine
Kar, A.; Häne, C.; and Malik, J. 2017 · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A.; and Gal, Y. 2017 · 2017
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Non-local recurrent neural memory for supervised sequence modeling
Fu, C.; Pei, W.; Cao, Q.; Zhang, C.; Zhao, Y.; Shen, X.; and Tai, Y.-W. 2019 · 2019
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P-mvsnet: Learning patch-wise matching confidence aggregation for multi-view stereo
Luo, K.; Guan, T.; Ju, L.; Huang, H.; and Luo, Y. 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Multi-Scale Geometric Consistency Guided Multi-View Stereo
Xu, Q.; and Tao, W. 2019 · 2019
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Mvscrf: Learning multi-view stereo with conditional random fields
Xue, Y.; Chen, J.; Wan, W.; Huang, Y.; Yu, C.; Li, T.; and Bao, J. 2019 · 2019
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Tanks and Temples: Benchmarking Large-scale Scene Reconstruction
Knapitsch, A.; Park, J.; Zhou, Q.-Y.; and Koltun, V. 2017 · 2017
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2018 · 2018
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DeepMVS: Learning Multi-view Stereopsis
Huang, P.; Matzen, K.; Kopf, J.; Ahuja, N.; and Huang, J. 2018 · 2018
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Non-local neural networks
Wang, X.; Girshick, R.; Gupta, A.; and He, K. 2018 · 2018
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Cbam: Convolutional block attention module
Woo, S.; Park, J.; Lee, J.-Y.; and Kweon, I. S. 2018 · 2018
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MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao, Y.; Luo, Z.; Li, S.; Fang, T.; and Quan, L. 2018 · 2018
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Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao, Y.; Luo, Z.; Li, S.; Shen, T.; Fang, T.; and Quan, L. 2019 · 2019
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Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness
Cheng, S.; Xu, Z.; Zhu, S.; Li, Z.; Li, L. E.; Ramamoorthi, R.; and Su, H. 2020 · 2020
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Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
Gu, X.; Fan, Z.; Zhu, S.; Dai, Z.; Tan, F.; and Tan, P. 2020 · 2020
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Attention-Aware Multi-View Stereo
Luo, K.; Guan, T.; Ju, L.; Wang, Y.; Chen, Z.; and Luo, Y. 2020 · 2020
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Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
Yan, J.; Wei, Z.; Yi, H.; Ding, M.; Zhang, R.; Chen, Y.; Wang, G.; and Tai, Y.-W. 2020 · 2020
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Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
Yang, J.; Mao, W.; Alvarez, J. M.; and Liu, M. 2020 · 2020
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PatchmatchNet: Learned Multi-View Patchmatch Stereo
Wang, F.; Galliani, S.; Vogel, C.; Speciale, P.; and Pollefeys, M. 2021 · 2021
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