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In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
Prism: A practical mealtime imaging stereo matcher
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Multi-resolution methods and graduated non-convexity
Neil, T., Tim, C.: · 1997
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Computing visual correspondence with occlusions using graph cuts
Kolmogorov, V., Zabih, R.: · 2001
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D., Szeliski, R.: · 2002
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Locally adaptive support-weight approach for visual correspondence search
Yoon, K.J., Kweon, I.S.: · 2005
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Efficient belief propagation for early vision
Felzenszwalb, P.F., Huttenlocher, D.P.: · 2006
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Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure
Klaus, A., Sormann, M., Karner, K.: · 2006
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Adaptive support-weight approach for correspondence search
Yoon, K.J., Kweon, I.S.: · 2006
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Simple but effective tree structures for dynamic programming-based stereo matching
Bleyer, M., Gelautz, M.: · 2008
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Stereo processing by semiglobal matching and mutual information
Hirschmuller, H.: · 2008
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Practical poissonian-gaussian noise modeling and fitting for single-image raw-data
Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: · 2008
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Projected texture stereo
Konolige, K.: · 2010
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Computer Vision: Algorithms and Applications. 1st edn
Szeliski, R.: · 2010
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Patchmatch stereo-stereo matching with slanted support windows
Bleyer, M., Rhemann, C., Rother, C.: · 2011
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T., Hinton, G.: · 2012
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
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Keep it simple and sparse: Real-time action recognition
Fanello, S.R., Gori, I., Metta, G., Odone, F.: · 2013
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One-shot learning for real-time action recognition
Fanello, S., Gori, I., Metta, G., Odone, F.: · 2013
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Fast cost-volume filtering for visual correspondence and beyond
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., Gelautz, M.: · 2013
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Real-time stereo matching on cuda using an iterative refinement method for adaptive support-weight correspondences
Kowalczuk, J., Psota, E.T., Perez, L.C.: · 2013
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Resolving multi-path interference in time-of-flight imaging via modulation frequency diversity and sparse regularization
Bhandari, A., Kadambi, A., Whyte, R., Barsi, C., Feigin, M., Dorrington, A., Raskar, R.: · 2014
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Resolving multipath interference in kinect: An inverse problem approach
Bhandari, A., Feigin, M., Izadi, S., Rhemann, C., Schmidt, M., Raskar, R.: · 2014
Cited alongside, same era.
Pmbp: Patchmatch belief propagation for correspondence field estimation
Besse, F., Rother, C., Fitzgibbon, A., Kautz, J.: · 2014
Cited alongside, same era.
Learning to be a depth camera for close-range human capture and interaction
Fanello, S.R., Keskin, C., Izadi, S., Kohli, P., Kim, D., Sweeney, D., Criminisi, A., Shotton, J., Kang, S., Paek, T.: · 2014
Cited alongside, same era.
High-resolution stereo datasets with subpixel-accurate ground truth
Scharstein, D., Hirschmuller, H., Kitajima, Y., Krathwohl, G., Nesic, N., Wang, X., Westling, P.: · 2014
Cited alongside, same era.
A light transport model for mitigating multipath interference in TOF sensors
Naik, N., Kadambi, A., Rhemann, C., Izadi, S., Raskar, R., Kang, S.: · 2015
Cited alongside, same era.
N.Mayer, E.Ilg, P.Häusser, P.Fischer, D.Cremers, A.Dosovitskiy, T.Brox: · 2016
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Motion2fusion: Real-time volumetric performance capture
Dou, M., Davidson, P., Fanello, S.R., Khamis, S., Kowdle, A., Rhemann, C., Tankovich, V., Izadi, S.: · 2017
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Articulated distance fields for ultra-fast tracking of hands interacting
Taylor, J., Tankovich, V., Tang, D., Keskin, C., Kim, D., Davidson, P., Kowdle, A., Izadi, S.: · 2017
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Ultrastereo: Efficient learning-based matching for active stereo systems
Fanello, S.R., Valentin, J., Rhemann, C., Kowdle, A., Tankovich, V., Davidson, P., Izadi, S.: · 2017
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Self-supervised learning for stereo matching with self-improving ability
Zhong, Y., Dai, Y., Li, H.: · 2017
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Zagoruyko, S., Komodakis, N.: · 2015
Cited alongside, same era.
Computing the stereo matching cost with a convolutional neural network
Zbontar, J., LeCun, Y.: · 2015
Cited alongside, same era.
Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: · 2015
Cited alongside, same era.
Object scene flow for autonomous vehicles
Menze, M., Geiger, A.: · 2015
Cited alongside, same era.
Fusion4d: Real-time performance capture of challenging scenes
Dou, M., Khamis, S., Degtyarev, Y., Davidson, P., Fanello, S.R., Kowdle, A., Escolano, S.O., Rhemann, C., Kim, D., Taylor, J., Kohli, P., Tankovich, V., Izadi, S.: · 2016
Cited alongside, same era.
Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences
Taylor, J., Bordeaux, L., Cashman, T., Corish, B., Keskin, C., Sharp, T., Soto, E., Sweeney, D., Valentin, J., Luff, B., Topalian, A., Wood, E., Khamis, S., Kohli, P., Izadi, S., Banks, R., Fitzgibbon, A., Shotton, J.: · 2016
Cited alongside, same era.
Hyperdepth: Learning depth from structured light without matching
Fanello, S.R., Rhemann, C., Tankovich, V., Kowdle, A., Orts Escolano, S., Kim, D., Izadi, S.: · 2016
Cited alongside, same era.
Later among the works it cites.
Improved stereo matching with constant highway networks and reflective confidence learning
Shaked, A., Wolf, L.: · 2017
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End-to-end learning of geometry and context for deep stereo regression
Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., Bry, A.: · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: · 2017
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Cascade residual learning: A two-stage convolutional neural network for stereo matching
Pang, J., Sun, W., Ren, J., Yang, C., Yan, Q.: · 2017
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Learning deep correspondence through prior and posterior feature constancy
Liang, Z., Feng, Y., Guo, Y., Liu, H., Qiao, L., Chen, W., Zhou, L., Zhang, J.: · 2017
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Detect, replace, refine: Deep structured prediction for pixel wise labeling
Gidaris, S., Komodakis, N.: · 2017
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Unsupervised monocular depth estimation with left-right consistency
Godard, C., Mac Aodha, O., Brostow, G.J.: · 2017
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Semi-supervised deep learning for monocular depth map prediction
Kuznietsov, Y., Stückler, J., Leibe, B.: · 2017
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Unsupervised learning of depth and ego-motion from video
Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: · 2017
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Intel RealSense Stereoscopic Depth Cameras
Keselman, L., Iselin Woodfill, J., Grunnet-Jepsen, A., Bhowmik, A.: · 2017
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Low compute and fully parallel computer vision with hashmatch
Fanello, S.R., Valentin, J., Kowdle, A., Rhemann, C., Tankovich, V., Ciliberto, C., Davidson, P., Izadi, S.: · 2017
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Loss functions for image restoration with neural networks
Zhao, H., Gallo, O., Frosio, I., Kautz, J.: · 2017
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Intel realsense d435
: · 2018
Closest in time.
Intel realsense d415
: · 2018
Closest in time.
Sos: Stereo matching in o(1) with slanted support windows
Tankovich, V., Schoenberg, M., Fanello, S.R., Kowdle, A., Rhemann, C., Dzitsiuk, M., Schmidt, M., Valentin, J., Izadi, S.: · 2018
Closest in time.
Stereonet: Guided hierarchical refinement for edge-aware depth prediction
Khamis, S., Fanello, S., Rhemann, C., Valentin, J., Kowdle, A., Izadi, S.: · 2018
Closest in time.