{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:28:43Z","timestamp":1774880923024,"version":"3.50.1"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319541891","type":"print"},{"value":"9783319541907","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-54190-7_13","type":"book-chapter","created":{"date-parts":[[2017,3,11]],"date-time":"2017-03-11T00:44:09Z","timestamp":1489193049000},"page":"207-224","source":"Crossref","is-referenced-by-count":16,"title":["Deep Discrete Flow"],"prefix":"10.1007","author":[{"given":"Fatma","family":"G\u00fcney","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Geiger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,12]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, H., Geiger, A., Urtasun, R.: Understanding high-level semantics by modeling traffic patterns. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2013)","DOI":"10.1109\/ICCV.2013.379"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nbein, M., Geiger, A.: Omnidirectional 3d reconstruction in augmented manhattan worlds. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS) (2014)","DOI":"10.1109\/IROS.2014.6942637"},{"key":"13_CR3","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/TPAMI.2013.185","volume":"36","author":"A Geiger","year":"2014","unstructured":"Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.: 3D traffic scene understanding from movable platforms. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 36, 1012\u20131025 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"13_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-642-33783-3_44","volume-title":"Computer Vision \u2013 ECCV 2012","author":"DJ Butler","year":"2012","unstructured":"Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611\u2013625. Springer, Heidelberg (2012). doi: 10.1007\/978-3-642-33783-3_44"},{"key":"13_CR7","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BKP Horn","year":"1981","unstructured":"Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. (AI) 17, 185\u2013203 (1981)","journal-title":"Artif. Intell. (AI)"},{"key":"13_CR8","unstructured":"Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (1981)"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Black, M.J., Anandan, P.: A framework for the robust estimation of optical flow. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (1993)","DOI":"10.1109\/ICCV.1993.378214"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-540-24673-2_3","volume-title":"Computer Vision - ECCV 2004","author":"T Brox","year":"2004","unstructured":"Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25\u201336. Springer, Heidelberg (2004). doi: 10.1007\/978-3-540-24673-2_3"},{"key":"13_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-319-24947-6_2","volume-title":"Pattern Recognition","author":"M Menze","year":"2015","unstructured":"Menze, M., Heipke, C., Geiger, A.: Discrete optimization for optical flow. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 16\u201328. Springer, Cham (2015). doi: 10.1007\/978-3-319-24947-6_2"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Q., Koltun, V.: Full flow: optical flow estimation by global optimization over regular grids. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.509"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Wulff, J., Black, M.J.: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298607"},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/978-3-319-10578-9_15","volume-title":"Computer Vision \u2013 ECCV 2014","author":"M Horn\u00e1\u010dek","year":"2014","unstructured":"Horn\u00e1\u010dek, M., Besse, F., Kautz, J., Fitzgibbon, A., Rother, C.: Highly overparameterized optical flow using patchmatch belief propagation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 220\u2013234. Springer, Cham (2014). doi: 10.1007\/978-3-319-10578-9_15"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"G\u00fcney, F., Geiger, A.: Displets: resolving stereo ambiguities using object knowledge. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7299044"},{"key":"13_CR16","first-page":"1","volume":"17","author":"J \u017dbontar","year":"2016","unstructured":"\u017dbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. (JMLR) 17, 1\u201332 (2016)","journal-title":"J. Mach. Learn. Res. (JMLR)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Chen, Z., Sun, X., Wang, L., Yu, Y., Huang, C.: A deep visual correspondence embedding model for stereo matching costs. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.117"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Luo, W., Schwing, A., Urtasun, R.: Efficient deep learning for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.614"},{"key":"13_CR19","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: Proceedings of the International Conference on Learning Representations (ICLR) (2016)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: edge-preserving interpolation of correspondences for optical flow. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298720"},{"key":"13_CR21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1023\/B:VISI.0000045324.43199.43","volume":"61","author":"A Bruhn","year":"2005","unstructured":"Bruhn, A., Weickert, J., Schn\u00f6rr, C.: Lucas\/Kanade meets Horn\/Schunck: combining local and global optic flow methods. Int. J. Comput. Vis. (IJCV) 61, 211\u2013231 (2005)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"13_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-319-10590-1_30","volume-title":"Computer Vision \u2013 ECCV 2014","author":"O Demetz","year":"2014","unstructured":"Demetz, O., Stoll, M., Volz, S., Weickert, J., Bruhn, A.: Learning brightness transfer functions for the joint recovery of illumination changes and optical flow. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 455\u2013471. Springer, Cham (2014). doi: 10.1007\/978-3-319-10590-1_30"},{"key":"13_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-3-319-10590-1_29","volume-title":"Computer Vision \u2013 ECCV 2014","author":"R Ranftl","year":"2014","unstructured":"Ranftl, R., Bredies, K., Pock, T.: Non-local total generalized variation for optical flow estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 439\u2013454. Springer, Cham (2014). doi: 10.1007\/978-3-319-10590-1_29"},{"key":"13_CR24","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11263-013-0644-x","volume":"106","author":"D Sun","year":"2013","unstructured":"Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. (IJCV) 106, 115\u2013137 (2013)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. In: Proceedings of the British Machine Vision Conference (BMVC) (2009)","DOI":"10.5244\/C.23.108"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. In: Pattern Recognition Letters, pp. 214\u2013223. Springer, Heidelberg (2007)","DOI":"10.1007\/978-3-540-74936-3_22"},{"key":"13_CR27","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/s11263-011-0422-6","volume":"93","author":"H Zimmer","year":"2011","unstructured":"Zimmer, H., Bruhn, A., Weickert, J.: Optic flow in harmony. Int. J. Comput. Vis. (IJCV) 93, 368\u2013388 (2011)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"13_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11263-010-0390-2","volume":"92","author":"S Baker","year":"2011","unstructured":"Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. (IJCV) 92, 1\u201331 (2011)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Braux-Zin, J., Dupont, R., Bartoli, A.: A general dense image matching framework combining direct and feature-based costs. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2013)","DOI":"10.1109\/ICCV.2013.30"},{"key":"13_CR30","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/TPAMI.2010.143","volume":"33","author":"T Brox","year":"2011","unstructured":"Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 33, 500\u2013513 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Timofte, R., Gool, L.V.: Sparse flow: sparse matching for small to large displacement optical flow. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2015)","DOI":"10.1109\/WACV.2015.151"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: DeepFlow: large displacement optical flow with deep matching. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2013)","DOI":"10.1109\/ICCV.2013.175"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Steinbr\u00fccker, F., Pock, T., Cremers, D.: Large displacement optical flow computation without warping. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1609\u20131614 (2009)","DOI":"10.1109\/ICCV.2009.5459364"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Yamaguchi, K., McAllester, D., Urtasun, R.: Robust monocular epipolar flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)","DOI":"10.1109\/CVPR.2013.243"},{"key":"13_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1007\/978-3-319-10602-1_49","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K Yamaguchi","year":"2014","unstructured":"Yamaguchi, K., McAllester, D., Urtasun, R.: Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 756\u2013771. Springer, Cham (2014). doi: 10.1007\/978-3-319-10602-1_49"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Lempitsky, V.S., Roth, S., Rother, C.: Fusionflow: discrete-continuous optimization for optical flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008)","DOI":"10.1109\/CVPR.2008.4587751"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Chen, Z., Jin, H., Lin, Z., Cohen, S., Wu, Y.: Large displacement optical flow from nearest neighbor fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)","DOI":"10.1109\/CVPR.2013.316"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Yang, J., Li, H.: Dense, accurate optical flow estimation with piecewise parametric model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298704"},{"key":"13_CR39","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/TIP.2013.2244221","volume":"22","author":"M Mozerov","year":"2013","unstructured":"Mozerov, M.: Constrained optical flow estimation as a matching problem. IEEE Trans. Image Process. (TIP) 22, 2044\u20132055 (2013)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"13_CR40","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11263-013-0653-9","volume":"110","author":"F Besse","year":"2014","unstructured":"Besse, F., Rother, C., Fitzgibbon, A., Kautz, J.: PMBP: patchmatch belief propagation for correspondence field estimation. Int. J. Comput. Vis. (IJCV) 110, 2\u201313 (2014)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"13_CR41","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TPAMI.2009.77","volume":"32","author":"E Tola","year":"2010","unstructured":"Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 32, 815\u2013830 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"13_CR42","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS) (2012)"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Fischer, P., Dosovitskiy, A., Ilg, E., H\u00e4usser, P., Hazirbas, C., Smagt, V.G.P., Cremers, D., Brox, T.: FlowNet: learning optical flow with convolutional networks. arXiv.org:1504.06852 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Mayer, N., Ilg, E., Haeusser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.438"},{"key":"13_CR45","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: Matchnet: unifying feature and metric learning for patch-based matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)"},{"key":"13_CR46","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.22"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"13_CR48","unstructured":"Bai, M., Luo, W., Kundu, K., Urtasun, R.: Deep semantic matching for optical flow. arXiv.org:1604.01827 (2016)"},{"key":"13_CR49","doi-asserted-by":"crossref","unstructured":"Gadot, D., Wolf, L.: Patchbatch: a batch augmented loss for optical flow. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.459"},{"key":"13_CR50","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2016"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-54190-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T12:31:11Z","timestamp":1568896271000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-54190-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319541891","9783319541907"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-54190-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}