{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:14:38Z","timestamp":1780053278891,"version":"3.54.0"},"publisher-location":"Cham","reference-count":104,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031727603","type":"print"},{"value":"9783031727610","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72761-0_17","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T07:01:50Z","timestamp":1727593310000},"page":"294-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Temporal Event Stereo via\u00a0Joint Learning with\u00a0Stereoscopic Flow"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0896-6793","authenticated-orcid":false,"given":"Hoonhee","family":"Cho","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9537-3813","authenticated-orcid":false,"given":"Jae-Young","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1634-2756","authenticated-orcid":false,"given":"Kuk-Jin","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, S.H., Jang, H.W., Uddin, S.N., Jung, Y.J.: Deep event stereo leveraged by event-to-image translation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 882\u2013890 (2021)","DOI":"10.1609\/aaai.v35i2.16171"},{"issue":"7","key":"17_CR2","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/TPAMI.2020.2986748","volume":"42","author":"M Almatrafi","year":"2020","unstructured":"Almatrafi, M., Baldwin, R., Aizawa, K., Hirakawa, K.: Distance surface for event-based optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 42(7), 1547\u20131556 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Alonso, I., Murillo, A.C.: EV-SegNet: semantic segmentation for event-based cameras. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00205"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Bardow, P., Davison, A.J., Leutenegger, S.: Simultaneous optical flow and intensity estimation from an event camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 884\u2013892 (2016)","DOI":"10.1109\/CVPR.2016.102"},{"issue":"2","key":"17_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1109\/TNNLS.2013.2273537","volume":"25","author":"R Benosman","year":"2013","unstructured":"Benosman, R., Clercq, C., Lagorce, X., Ieng, S.H., Bartolozzi, C.: Event-based visual flow. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 407\u2013417 (2013)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR6","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1109\/JSSC.2014.2342715","volume":"49","author":"C Brandli","year":"2014","unstructured":"Brandli, C., Berner, R., Yang, M., Liu, S.C., Delbruck, T.: A 240 $$\\times $$ 180 130 db 3 $$\\upmu $$s latency global shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49, 2333\u20132341 (2014)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"17_CR7","first-page":"48","volume":"8","author":"LA Camunas-Mesa","year":"2014","unstructured":"Camunas-Mesa, L.A., Serrano-Gotarredona, T., Ieng, S.H., Benosman, R.B., Linares-Barranco, B.: On the use of orientation filters for 3D reconstruction in event-driven stereo vision. Front. Neurosci. 8, 48 (2014)","journal-title":"Front. Neurosci."},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, S., et al.: Deep stereo using adaptive thin volume representation with uncertainty awareness. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2524\u20132534 (2020)","DOI":"10.1109\/CVPR42600.2020.00260"},{"key":"17_CR11","unstructured":"Cheng, X., et al.: Hierarchical neural architecture search for deep stereo matching. ArXiv abs\/2010.13501 (2020)"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Cho, H., Cho, J., Yoon, K.J.: Learning adaptive dense event stereo from the image domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17797\u201317807 (2023)","DOI":"10.1109\/CVPR52729.2023.01707"},{"issue":"4","key":"17_CR13","doi-asserted-by":"publisher","first-page":"6709","DOI":"10.1109\/LRA.2021.3096161","volume":"6","author":"H Cho","year":"2021","unstructured":"Cho, H., Jeong, J., Yoon, K.J.: EOMVS: event-based omnidirectional multi-view stereo. IEEE Robot. Autom. Lett. 6(4), 6709\u20136716 (2021). https:\/\/doi.org\/10.1109\/LRA.2021.3096161","journal-title":"IEEE Robot. Autom. Lett."},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Cho, H., Jeong, Y., Kim, T., Yoon, K.J.: Non-coaxial event-guided motion deblurring with spatial alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12492\u201312503 (2023)","DOI":"10.1109\/ICCV51070.2023.01148"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Cho, H., Kim, H., Chae, Y., Yoon, K.J.: Label-free event-based object recognition via joint learning with image reconstruction from events. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19866\u201319877 (2023)","DOI":"10.1109\/ICCV51070.2023.01819"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Cho, H., Kim, T., Jeong, Y., Yoon, K.J.: TTA-EVF: test-time adaptation for event-based video frame interpolation via reliable pixel and sample estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25701\u201325711 (2024)","DOI":"10.1109\/CVPR52733.2024.02428"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Cho, H., Yoon, K.J.: Event-image fusion stereo using cross-modality feature propagation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 454\u2013462 (2022)","DOI":"10.1609\/aaai.v36i1.19923"},{"key":"17_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/978-3-031-19824-3_28","volume-title":"Computer Vision - ECCV 2022","author":"H Cho","year":"2022","unstructured":"Cho, H., Yoon, K.J.: Selection and cross similarity for event-image deep stereo. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 470\u2013486. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19824-3_28"},{"key":"17_CR19","unstructured":"Choi, J., Yoon, K.J., et\u00a0al.: Learning to super resolve intensity images from events. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2768\u20132776 (2020)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Ding, Z., et al.: Spatio-temporal recurrent networks for event-based optical flow estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 525\u2013533 (2022)","DOI":"10.1609\/aaai.v36i1.19931"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Fox, G., Pan, X., Tewari, A., Elgharib, M., Theobalt, C.: Unsupervised event-based video reconstruction. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4179\u20134188 (2024)","DOI":"10.1109\/WACV57701.2024.00413"},{"issue":"6","key":"17_CR22","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1016\/S0893-6080(05)80125-X","volume":"6","author":"KI Funahashi","year":"1993","unstructured":"Funahashi, K.I., Nakamura, Y.: Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 6(6), 801\u2013806 (1993)","journal-title":"Neural Netw."},{"issue":"1","key":"17_CR23","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","volume":"44","author":"G Gallego","year":"2020","unstructured":"Gallego, G., et al.: Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 154\u2013180 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Gallego, G., Rebecq, H., Scaramuzza, D.: A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3867\u20133876 (2018)","DOI":"10.1109\/CVPR.2018.00407"},{"issue":"3","key":"17_CR25","doi-asserted-by":"publisher","first-page":"4947","DOI":"10.1109\/LRA.2021.3068942","volume":"6","author":"M Gehrig","year":"2021","unstructured":"Gehrig, M., Aarents, W., Gehrig, D., Scaramuzza, D.: DSEC: a stereo event camera dataset for driving scenarios. IEEE Robot. Autom. Lett. 6(3), 4947\u20134954 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Gehrig, M., Millh\u00e4usler, M., Gehrig, D., Scaramuzza, D.: E-RAFT: dense optical flow from event cameras. In: 2021 International Conference on 3D Vision (3DV), pp. 197\u2013206. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00030"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Gehrig, M., Muglikar, M., Scaramuzza, D.: Dense continuous-time optical flow from event cameras. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3361671"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3268\u20133277 (2019)","DOI":"10.1109\/CVPR.2019.00339"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Han, J., et al.: Neuromorphic camera guided high dynamic range imaging. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1730\u20131739 (2020)","DOI":"10.1109\/CVPR42600.2020.00180"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Hu, L., et al.: Optical flow estimation for spiking camera. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17844\u201317853 (2022)","DOI":"10.1109\/CVPR52688.2022.01732"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Hur, J., Roth, S.: Iterative residual refinement for joint optical flow and occlusion estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5754\u20135763 (2019)","DOI":"10.1109\/CVPR.2019.00590"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462\u20132470 (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Jiang, S., Campbell, D., Lu, Y., Li, H., Hartley, R.: Learning to estimate hidden motions with global motion aggregation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9772\u20139781 (2021)","DOI":"10.1109\/ICCV48922.2021.00963"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Zhang, Y., Zou, D., Ren, J., Lv, J., Liu, Y.: Learning event-based motion deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3320\u20133329 (2020)","DOI":"10.1109\/CVPR42600.2020.00338"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P.: End-to-end learning of geometry and context for deep stereo regression. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 66\u201375 (2017)","DOI":"10.1109\/ICCV.2017.17"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Kim, T., Chae, Y., Jang, H.K., Yoon, K.J.: Event-based video frame interpolation with cross-modal asymmetric bidirectional motion fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18032\u201318042 (2023)","DOI":"10.1109\/CVPR52729.2023.01729"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Kim, T., Cho, H., Yoon, K.J.: Frequency-aware event-based video deblurring for real-world motion blur. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24966\u201324976 (2024)","DOI":"10.1109\/CVPR52733.2024.02358"},{"key":"17_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1007\/978-3-642-24028-7_62","volume-title":"Advances in Visual Computing","author":"J Kogler","year":"2011","unstructured":"Kogler, J., Humenberger, M., Sulzbachner, C.: Event-based stereo matching approaches for frameless address event stereo data. In: Bebis, G., et al. (eds.) ISVC 2011. LNCS, vol. 6938, pp. 674\u2013685. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24028-7_62"},{"key":"17_CR40","unstructured":"Laga, H., Jospin, L.V., Boussa\u00efd, F., Bennamoun, M.: A survey on deep learning techniques for stereo-based depth estimation. IEEE Trans. Pattern Anal. Mach. Intell. PP (2020)"},{"issue":"7","key":"17_CR41","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1109\/TPAMI.2016.2574707","volume":"39","author":"X Lagorce","year":"2016","unstructured":"Lagorce, X., Orchard, G., Galluppi, F., Shi, B.E., Benosman, R.B.: HOTS: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1346\u20131359 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR42","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Practical stereo matching via cascaded recurrent network with adaptive correlation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16263\u201316272 (2022)","DOI":"10.1109\/CVPR52688.2022.01578"},{"key":"17_CR43","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","volume":"43","author":"P Lichtsteiner","year":"2008","unstructured":"Lichtsteiner, P., Posch, C., Delbr\u00fcck, T.: A 128 $$\\times $$ 128 120 db 15 $$\\upmu $$s latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circuits 43, 566\u2013576 (2008)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"17_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/978-3-030-58598-3_41","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Lin","year":"2020","unstructured":"Lin, S., et al.: Learning event-driven video deblurring and interpolation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 695\u2013710. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_41"},{"key":"17_CR45","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: TMA: temporal motion aggregation for event-based optical flow. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00888"},{"key":"17_CR46","unstructured":"Liu, M., Delbruck, T.: Adaptive time-slice block-matching optical flow algorithm for dynamic vision sensors. In: BMVC (2018)"},{"key":"17_CR47","doi-asserted-by":"crossref","unstructured":"Luo, X., Luo, K., Luo, A., Wang, Z., Tan, P., Liu, S.: Learning optical flow from event camera with rendered dataset. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9847\u20139857 (2023)","DOI":"10.1109\/ICCV51070.2023.00903"},{"key":"17_CR48","doi-asserted-by":"crossref","unstructured":"Manderscheid, J., Sironi, A., Bourdis, N., Migliore, D., Lepetit, V.: Speed invariant time surface for learning to detect corner points with event-based cameras. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10245\u201310254 (2019)","DOI":"10.1109\/CVPR.2019.01049"},{"key":"17_CR49","doi-asserted-by":"crossref","unstructured":"Maqueda, A.I., Loquercio, A., Gallego, G., Garc\u00eda, N., Scaramuzza, D.: Event-based vision meets deep learning on steering prediction for self-driving cars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5419\u20135427 (2018)","DOI":"10.1109\/CVPR.2018.00568"},{"issue":"10","key":"17_CR50","doi-asserted-by":"publisher","first-page":"6890","DOI":"10.1109\/TPAMI.2021.3096985","volume":"44","author":"M Mostafavi","year":"2021","unstructured":"Mostafavi, M., Nam, Y., Choi, J., Yoon, K.J.: E2SRI: learning to super-resolve intensity images from events. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6890\u20136909 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR51","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1007\/s11263-020-01410-2","volume":"129","author":"M Mostafavi","year":"2021","unstructured":"Mostafavi, M., Wang, L., Yoon, K.J.: Learning to reconstruct HDR images from events, with applications to depth and flow prediction. Int. J. Comput. Vision 129, 900\u2013920 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"17_CR52","doi-asserted-by":"crossref","unstructured":"Mostafavi, M., Yoon, K.J., Choi, J.: Event-intensity stereo: estimating depth by the best of both worlds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4258\u20134267 (2021)","DOI":"10.1109\/ICCV48922.2021.00422"},{"key":"17_CR53","doi-asserted-by":"crossref","unstructured":"Nam, Y., Mostafavi, M., Yoon, K.J., Choi, J.: Stereo depth from events cameras: concentrate and focus on the future. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Patter Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00602"},{"key":"17_CR54","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Do, T.T., Caldwell, D.G., Tsagarakis, N.G.: Real-time 6DOF pose relocalization for event cameras with stacked spatial LSTM networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00207"},{"key":"17_CR55","doi-asserted-by":"crossref","unstructured":"Pan, L., Liu, M., Hartley, R.: Single image optical flow estimation with an event camera. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1669\u20131678. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00174"},{"key":"17_CR56","doi-asserted-by":"crossref","unstructured":"Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., Dai, Y.: Bringing a blurry frame alive at high frame-rate with an event camera. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6820\u20136829 (2019)","DOI":"10.1109\/CVPR.2019.00698"},{"key":"17_CR57","doi-asserted-by":"crossref","unstructured":"Paredes-Vall\u00e9s, F., de\u00a0Croon, G.C.: Back to event basics: self-supervised learning of image reconstruction for event cameras via photometric constancy. arXiv preprint arXiv:2009.08283 (2020)","DOI":"10.1109\/CVPR46437.2021.00345"},{"issue":"8","key":"17_CR58","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TPAMI.2019.2903179","volume":"42","author":"F Paredes-Vall\u00e9s","year":"2019","unstructured":"Paredes-Vall\u00e9s, F., Scheper, K.Y., De Croon, G.C.: Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: from events to global motion perception. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2051\u20132064 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR59","doi-asserted-by":"crossref","unstructured":"Paredes-Vall\u00e9s, F., Scheper, K.Y., De\u00a0Wagter, C., de\u00a0Croon, G.C.: Taming contrast maximization for learning sequential, low-latency, event-based optical flow. arXiv preprint arXiv:2303.05214 (2023)","DOI":"10.1109\/ICCV51070.2023.00889"},{"issue":"6","key":"17_CR60","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/TPAMI.2018.2837760","volume":"41","author":"MG Park","year":"2018","unstructured":"Park, M.G., Yoon, K.J.: Learning and selecting confidence measures for robust stereo matching. IEEE Trans. Pattern Anal. Mach. Intell. 41(6), 1397\u20131411 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR61","first-page":"16639","volume":"33","author":"E Perot","year":"2020","unstructured":"Perot, E., De Tournemire, P., Nitti, D., Masci, J., Sironi, A.: Learning to detect objects with a 1 megapixel event camera. Adv. Neural. Inf. Process. Syst. 33, 16639\u201316652 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR62","doi-asserted-by":"crossref","unstructured":"Piatkowska, E., Belbachir, A., Gelautz, M.: Asynchronous stereo vision for event-driven dynamic stereo sensor using an adaptive cooperative approach. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 45\u201350 (2013)","DOI":"10.1109\/ICCVW.2013.13"},{"key":"17_CR63","doi-asserted-by":"crossref","unstructured":"Piatkowska, E., Kogler, J., Belbachir, N., Gelautz, M.: Improved cooperative stereo matching for dynamic vision sensors with ground truth evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 53\u201360 (2017)","DOI":"10.1109\/CVPRW.2017.51"},{"key":"17_CR64","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TPAMI.2019.2963386","volume":"43","author":"H Rebecq","year":"2019","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: High speed and high dynamic range video with an event camera. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1964\u20131980 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"17_CR65","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1109\/TNNLS.2011.2180025","volume":"23","author":"P Rogister","year":"2011","unstructured":"Rogister, P., Benosman, R., Ieng, S.H., Lichtsteiner, P., Delbruck, T.: Asynchronous event-based binocular stereo matching. IEEE Trans. Neural Netw. Learn. Syst. 23(2), 347\u2013353 (2011)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR66","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2004","unstructured":"Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47, 7\u201342 (2004)","journal-title":"Int. J. Comput. Vision"},{"key":"17_CR67","doi-asserted-by":"crossref","unstructured":"Shiba, S., Aoki, Y., Gallego, G.: Event collapse in contrast maximization frameworks. Sens. (Basel Switz.) 22 (2022). https:\/\/api.semanticscholar.org\/CorpusID:250408092","DOI":"10.3390\/s22145190"},{"key":"17_CR68","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-031-19797-0_36","volume-title":"Computer Vision - ECCV 2022","author":"S Shiba","year":"2022","unstructured":"Shiba, S., Aoki, Y., Gallego, G.: Secrets of event-based optical flow. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13678, pp. 628\u2013645. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_36"},{"key":"17_CR69","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"17_CR70","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/978-3-031-19797-0_24","volume-title":"Computer Vision - ECCV 2022","author":"L Sun","year":"2022","unstructured":"Sun, L., et al.: Event-based fusion for motion deblurring with cross-modal attention. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13678, pp. 412\u2013428. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_24"},{"key":"17_CR71","doi-asserted-by":"crossref","unstructured":"Sun, L., et al.: Event-based frame interpolation with ad-hoc deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18043\u201318052 (2023)","DOI":"10.1109\/CVPR52729.2023.01730"},{"key":"17_CR72","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-58536-5_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Teed","year":"2020","unstructured":"Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 402\u2013419. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_24"},{"key":"17_CR73","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Bochicchio, A., Gehrig, D., Georgoulis, S., Li, Y., Scaramuzza, D.: Time lens++: event-based frame interpolation with parametric non-linear flow and multi-scale fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17755\u201317764 (2022)","DOI":"10.1109\/CVPR52688.2022.01723"},{"key":"17_CR74","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Fleuret, F., Kiefel, M., Gehler, P., Hirsch, M.: Learning an event sequence embedding for dense event-based deep stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1527\u20131537 (2019)","DOI":"10.1109\/ICCV.2019.00161"},{"key":"17_CR75","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., et al.: Time lens: Event-based video frame interpolation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16155\u201316164 (2021)","DOI":"10.1109\/CVPR46437.2021.01589"},{"key":"17_CR76","doi-asserted-by":"publisher","first-page":"7237","DOI":"10.1109\/TIP.2022.3220938","volume":"31","author":"Z Wan","year":"2022","unstructured":"Wan, Z., Dai, Y., Mao, Y.: Learning dense and continuous optical flow from an event camera. IEEE Trans. Image Process. 31, 7237\u20137251 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR77","doi-asserted-by":"crossref","unstructured":"Wan, Z., Mao, Y., Zhang, J., Dai, Y.: RPEFlow: multimodal fusion of RGB-PointCloud-event for joint optical flow and scene flow estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10030\u201310040 (2023)","DOI":"10.1109\/ICCV51070.2023.00920"},{"key":"17_CR78","doi-asserted-by":"crossref","unstructured":"Wang, L., Chae, Y., Yoon, K.J.: Dual transfer learning for event-based end-task prediction via pluggable event to image translation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00214"},{"key":"17_CR79","doi-asserted-by":"crossref","unstructured":"Wang, L., Ho, Y.S., Yoon, K.J., et\u00a0al.: Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10081\u201310090 (2019)","DOI":"10.1109\/CVPR.2019.01032"},{"key":"17_CR80","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., et al.: CroCo v2: improved cross-view completion pre-training for stereo matching and optical flow. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17969\u201317980 (2023)","DOI":"10.1109\/ICCV51070.2023.01647"},{"key":"17_CR81","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, pp. 1385\u20131392 (2013)","DOI":"10.1109\/ICCV.2013.175"},{"issue":"11","key":"17_CR82","doi-asserted-by":"publisher","first-page":"3850","DOI":"10.1109\/TPAMI.2020.2992497","volume":"43","author":"C Won","year":"2020","unstructured":"Won, C., Ryu, J., Lim, J.: End-to-end learning for omnidirectional stereo matching with uncertainty prior. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3850\u20133862 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR83","doi-asserted-by":"crossref","unstructured":"Xia, R., Zhao, C., Zheng, M., Wu, Z., Sun, Q., Tang, Y.: CMDA: cross-modality domain adaptation for nighttime semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21572\u201321581 (2023)","DOI":"10.1109\/ICCV51070.2023.01972"},{"key":"17_CR84","doi-asserted-by":"crossref","unstructured":"Xu, F., et al.: Motion deblurring with real events. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2583\u20132592 (2021)","DOI":"10.1109\/ICCV48922.2021.00258"},{"key":"17_CR85","doi-asserted-by":"crossref","unstructured":"Xu, F., et al.: Motion deblurring with real events. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2583\u20132592 (2021)","DOI":"10.1109\/ICCV48922.2021.00258"},{"key":"17_CR86","doi-asserted-by":"crossref","unstructured":"Xu, G., Cheng, J., Guo, P., Yang, X.: Attention concatenation volume for accurate and efficient stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12981\u201312990 (2022)","DOI":"10.1109\/CVPR52688.2022.01264"},{"key":"17_CR87","doi-asserted-by":"crossref","unstructured":"Xu, G., Wang, X., Ding, X., Yang, X.: Iterative geometry encoding volume for stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21919\u201321928 (2023)","DOI":"10.1109\/CVPR52729.2023.02099"},{"key":"17_CR88","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, J.: AANet: adaptive aggregation network for efficient stereo matching. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1956\u20131965 (2020)","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"17_CR89","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, J.: AANet: adaptive aggregation network for efficient stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 1959\u20131968 (2020)","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"17_CR90","unstructured":"Ye, C., Mitrokhin, A., Ferm\u00fcller, C., Yorke, J.A., Aloimonos, Y.: Unsupervised learning of dense optical flow, depth and egomotion from sparse event data. arXiv preprint arXiv:1809.08625 (2018)"},{"key":"17_CR91","doi-asserted-by":"crossref","unstructured":"Zhang, F., Prisacariu, V.A., Yang, R., Torr, P.H.S.: GA-Net: guided aggregation net for end-to-end stereo matching. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 185\u2013194 (2019)","DOI":"10.1109\/CVPR.2019.00027"},{"issue":"1","key":"17_CR92","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s11263-022-01697-3","volume":"131","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Li, S., Luo, Z., Fang, T., Yao, Y.: Vis-MVSNet: visibility-aware multi-view stereo network. Int. J. Comput. Vision 131(1), 199\u2013214 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"17_CR93","doi-asserted-by":"crossref","unstructured":"Zhang, K., et al.: Discrete time convolution for fast event-based stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8676\u20138686 (2022)","DOI":"10.1109\/CVPR52688.2022.00848"},{"key":"17_CR94","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-030-67832-6_29","volume-title":"MultiMedia Modeling","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Zhang, H., Zhu, C., Guo, S., Chen, J., Wang, L.: Fine-grained video deblurring with event camera. In: Loko\u010d, J., et al. (eds.) MMM 2021. LNCS, vol. 12572, pp. 352\u2013364. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67832-6_29"},{"key":"17_CR95","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1007\/978-3-030-58523-5_39","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Zhang, Yu., Jiang, Z., Zou, D., Ren, J., Zhou, B.: Learning to see in the dark with events. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 666\u2013682. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_39"},{"key":"17_CR96","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yu, L.: Unifying motion deblurring and frame interpolation with events. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17765\u201317774 (2022)","DOI":"10.1109\/CVPR52688.2022.01724"},{"key":"17_CR97","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Poggi, M., Mattoccia, S.: TemporalStereo: efficient spatial-temporal stereo matching network. In: 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9528\u20139535. IEEE (2023)","DOI":"10.1109\/IROS55552.2023.10341598"},{"key":"17_CR98","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Chen, Y., Daniilidis, K.: Realtime time synchronized event-based stereo. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 433\u2013447 (2018)","DOI":"10.1007\/978-3-030-01231-1_27"},{"issue":"3","key":"17_CR99","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1109\/LRA.2018.2800793","volume":"3","author":"AZ Zhu","year":"2018","unstructured":"Zhu, A.Z., Thakur, D., \u00d6zaslan, T., Pfrommer, B., Kumar, V., Daniilidis, K.: The multivehicle stereo event camera dataset: an event camera dataset for 3D perception. IEEE Robot. Autom. Lett. 3(3), 2032\u20132039 (2018). https:\/\/doi.org\/10.1109\/LRA.2018.2800793","journal-title":"IEEE Robot. Autom. Lett."},{"key":"17_CR100","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: EV-FlowNet: self-supervised optical flow estimation for event-based cameras. arXiv preprint arXiv:1802.06898 (2018)","DOI":"10.15607\/RSS.2018.XIV.062"},{"key":"17_CR101","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based learning of optical flow, depth, and egomotion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognitionm pp. 989\u2013997 (2019)","DOI":"10.1109\/CVPR.2019.00108"},{"key":"17_CR102","doi-asserted-by":"crossref","unstructured":"Zihao\u00a0Zhu, A., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based optical flow using motion compensation. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11024-6_54"},{"key":"17_CR103","doi-asserted-by":"crossref","unstructured":"Zou, D., et al.: Context-aware event-driven stereo matching. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1076\u20131080. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532523"},{"key":"17_CR104","doi-asserted-by":"crossref","unstructured":"Zou, D., et al.: Robust dense depth map estimation from sparse DVS stereos. In: British Machine Vision Conference (BMVC), vol.\u00a01 (2017)","DOI":"10.5244\/C.31.39"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72761-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T07:31:46Z","timestamp":1727595106000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72761-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031727603","9783031727610"],"references-count":104,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72761-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}