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We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision.
Surfaces from stereo: Integrating feature matching, disparity estimation, and contour detection
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A multibaseline stereo system with active illumination and real-time image acquisition
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Lehmann, E. L. and Casella, G · 1998
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Computing visual correspondence with occlusions using graph cuts
Kolmogorov, V. and Zabih, R · 2001
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The implicit function theorem: history, theory, and applications
Krantz, S. G. and Parks, H. R · 2002
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D. and Szeliski, R · 2002
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Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure
Klaus, A., Sormann, M., and Karner, K · 2006
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Stereo processing by semiglobal matching and mutual information
Hirschmuller, H · 2007
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Introduction to Algorithms, Third Edition
Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C · 2009
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Patchmatch stereo-stereo matching with slanted support windows
Bleyer, M., Rhemann, C., and Rother, C · 2011
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R · 2012
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Statistical decision theory and Bayesian analysis
Berger, J. O · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Efficient joint segmentation, occlusion labeling, stereo and flow estimation
Yamaguchi, K., McAllester, D., and Urtasun, R · 2014
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Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology
Bimbraw, K · 2015
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Object scene flow for autonomous vehicles
Menze, M. and Geiger, A · 2015
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Computing the stereo matching cost with a convolutional neural network
Zbontar, J. and LeCun, Y · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., and Brox, T · 2016
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Stacked hourglass networks for human pose estimation
Newell, A., Yang, K., and Deng, J · 2016
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A multi-view stereo benchmark with high-resolution images and multi-camera videos
Schöps, T., Schönberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., and Geiger, A · 2017
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Unsupervised adaptation for deep stereo
Tonioni, A., Poggi, M., Mattoccia, S., and Di Stefano, L · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J · 2017
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Pyramid stereo matching network
Chang, J.-R. and Chen, Y.-S · 2018
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Zoom and learn: Generalizing deep stereo matching to novel domains
Pang, J., Sun, W., Yang, C., Ren, J., Xiao, R., Zeng, J., and Lin, L · 2018
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On the over-smoothing problem of cnn based disparity estimation
Chen, C., Chen, X., and Cheng, H · 2019
Cited alongside, same era.
Deeppruner: Learning efficient stereo matching via differentiable patchmatch
Duggal, S., Wang, S., Ma, W.-C., Hu, R., and Urtasun, R · 2019
Cited alongside, same era.
Stereo-vision-based crop height estimation for agricultural robots
Kim, W.-S., Lee, D.-H., Kim, Y.-J., Kim, T., Lee, W.-S., and Choi, C.-H · 2021
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Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers
Li, Z., Liu, X., Drenkow, N., Ding, A., Creighton, F. X., Taylor, R. H., and Unberath, M · 2021
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Raft-stereo: Multilevel recurrent field transforms for stereo matching
Lipson, L., Teed, Z., and Deng, J · 2021
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Cfnet: Cascade and fused cost volume for robust stereo matching
Shen, Z., Dai, Y., and Rao, Z · 2021
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Adastereo: A simple and efficient approach for adaptive stereo matching
Song, X., Yang, G., Zhu, X., Zhou, H., Wang, Z., and Shi, J · 2021
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Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching
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Group-wise correlation stereo network
Guo, X., Yang, K., Yang, W., Wang, X., and Li, H · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
Cited alongside, same era.
Anytime stereo image depth estimation on mobile devices
Wang, Y., Lai, Z., Huang, G., Wang, B. H., Van Der Maaten, L., Campbell, M., and Weinberger, K. Q · 2019
Cited alongside, same era.
Ga-net: Guided aggregation net for end-to-end stereo matching
Zhang, F., Prisacariu, V., Yang, R., and Torr, P. H · 2019
Cited alongside, same era.
High-frequency stereo matching network
Zhao, H., Zhou, H., Zhang, Y., Chen, J., Yang, Y., and Zhao, Y · 2019
Cited alongside, same era.
Bi3d: Stereo depth estimation via binary classifications
Badki, A., Troccoli, A., Kim, K., Kautz, J., Sen, P., and Gallo, O · 2020
Cited alongside, same era.
Tankovich, V., Hane, C., Zhang, Y., Kowdle, A., Fanello, S., and Bouaziz, S · 2021
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Smd-nets: Stereo mixture density networks
Tosi, F., Liao, Y., Schmitt, C., and Geiger, A · 2021
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Itsa: An information-theoretic approach to automatic shortcut avoidance and domain generalization in stereo matching networks
Chuah, W., Tennakoon, R., Hoseinnezhad, R., Bab-Hadiashar, A., and Suter, D · 2022
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Practical stereo matching via cascaded recurrent network with adaptive correlation
Li, J., Wang, P., Xiong, P., Cai, T., Yan, Z., Yang, L., Liu, J., Fan, H., and Liu, S · 2022
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Floatingfusion: Depth from tof and image-stabilized stereo cameras
Meuleman, A., Kim, H., Tompkin, J., and Kim, M. H · 2022
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Rethinking depth estimation for multi-view stereo: A unified representation
Peng, R., Wang, R., Wang, Z., Lai, Y., and Wang, R · 2022
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Rethinking training strategy in stereo matching
Rao, Z., Dai, Y., Shen, Z., and He, R · 2022
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Pcw-net: Pyramid combination and warping cost volume for stereo matching
Shen, Z., Dai, Y., Song, X., Rao, Z., Zhou, D., and Zhang, L · 2022
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Computer vision: algorithms and applications
Szeliski, R · 2022
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Attention concatenation volume for accurate and efficient stereo matching
Xu, G., Cheng, J., Guo, P., and Yang, X · 2022
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Non-parametric depth distribution modelling based depth inference for multi-view stereo
Yang, J., Alvarez, J. M., and Liu, M · 2022
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Revisiting domain generalized stereo matching networks from a feature consistency perspective
Zhang, J., Wang, X., Bai, X., Wang, C., Huang, L., Chen, Y., Gu, L., Zhou, J., Harada, T., and Hancock, E. R · 2022
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Domain generalized stereo matching via hierarchical visual transformation
Chang, T., Yang, X., Zhang, T., and Wang, M · 2023
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Enhanced stable view synthesis
Jain, N., Kumar, S., and Van Gool, L · 2023
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Multi-view photometric stereo revisited
Kaya, B., Kumar, S., Oliveira, C., Ferrari, V., and Van Gool, L · 2023
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CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Weinzaepfel, P., Lucas, T., Leroy, V., Cabon, Y., Arora, V., Brégier, R., Csurka, G., Antsfeld, L., Chidlovskii, B., and Revaud, J · 2023
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Iterative geometry encoding volume for stereo matching
Xu, G., Wang, X., Ding, X., and Yang, X · 2023
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Learning robust multi-scale representation for neural radiance fields from unposed images
Jain, N., Kumar, S., and Van Gool, L · 2024
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