{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:25:54Z","timestamp":1777656354357,"version":"3.51.4"},"publisher-location":"Cham","reference-count":70,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200731","type":"print"},{"value":"9783031200748","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20074-8_32","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:11Z","timestamp":1668198191000},"page":"557-574","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Learning Omnidirectional Flow in\u00a0360$$^\\circ $$ Video via\u00a0Siamese Representation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-5948","authenticated-orcid":false,"given":"Keshav","family":"Bhandari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaowen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo","family":"Latapie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziliang","family":"Zong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"32_CR1","unstructured":"Adobe: Mixamo. www.mixamo.com\/"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Ahmadi, A., Patras, I.: Unsupervised convolutional neural networks for motion estimation. In: ICIP (2016)","DOI":"10.1109\/ICIP.2016.7532634"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Artizzu, C.O., Zhang, H., Allibert, G., Demonceaux, C.: OmniFlowNet: a perspective neural network adaptation for optical flow estimation in omnidirectional images. In: ICPR (2021)","DOI":"10.1109\/ICPR48806.2021.9412745"},{"issue":"8","key":"32_CR4","first-page":"2524","volume":"2019","author":"R Azevedo","year":"2020","unstructured":"Azevedo, R., Birkbeck, N., Simone, F., Janatra, I., Adsumilli, B., Frossard, P.: Visual distortions in 360-degree videos. TCSVT 2019(8), 2524\u20132537 (2020)","journal-title":"TCSVT"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Bailer, C., Taetz, B., Stricker, D.: Flow fields: dense correspondence fields for highly accurate large displacement optical flow estimation. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.457"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Baker, S., Roth, S., Scharstein, D., Black, M.J., Lewis, J., Szeliski, R.: A database and evaluation methodology for optical flow. In: ICCV (2007)","DOI":"10.1109\/ICCV.2007.4408903"},{"issue":"1","key":"32_CR7","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/BF01420984","volume":"12","author":"JL Barron","year":"1994","unstructured":"Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. IJCV 12(1), 43\u201377 (1994)","journal-title":"IJCV"},{"key":"32_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/978-3-319-48881-3_56","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"L Bertinetto","year":"2016","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional Siamese networks for object tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850\u2013865. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Bhandari, K., Zong, Z., Yan, Y.: Revisiting optical flow estimation in 360 videos. In: ICPR (2021)","DOI":"10.1109\/ICPR48806.2021.9412035"},{"key":"32_CR10","unstructured":"Blender: https:\/\/www.blender.org\/"},{"key":"32_CR11","unstructured":"Boomsma, W., Frellsen, J.: Spherical convolutions and their application in molecular modelling. In: NeurIPS (2017)"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Bromley, J., et al.: Signature verification using a \u201cSiamese\u201d time delay neural network. IJPRAI 7(04), 669\u2013688 (1993)","DOI":"10.1142\/S0218001493000339"},{"key":"32_CR13","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). https:\/\/doi.org\/10.1007\/978-3-540-24673-2_3"},{"issue":"3","key":"32_CR14","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1109\/TPAMI.2010.143","volume":"33","author":"T Brox","year":"2010","unstructured":"Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. TPAMI 33(3), 500\u2013513 (2010)","journal-title":"TPAMI"},{"key":"32_CR15","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). https:\/\/doi.org\/10.1007\/978-3-642-33783-3_44"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Chen, Q., Koltun, V.: Full flow: optical flow estimation by global optimization over regular grids. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.509"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple Siamese representation learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"32_CR18","unstructured":"Cohen, T.S., Geiger, M., Koehler, J., Welling, M.: Spherical CNNs. arXiv (2018)"},{"key":"32_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-030-01240-3_32","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Coors","year":"2018","unstructured":"Coors, B., Condurache, A.P., Geiger, A.: SphereNet: learning spherical representations for detection and classification in omnidirectional images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 525\u2013541. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_32"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Demonceaux, C., Kachi-Akkouche, D.: Optical flow estimation in omnidirectional images using wavelet approach. In: CVPRW (2003)","DOI":"10.1109\/CVPRW.2003.10080"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Eder, M., Shvets, M., Lim, J., Frahm, J.M.: Tangent images for mitigating spherical distortion. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01244"},{"key":"32_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-030-01261-8_4","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Esteves","year":"2018","unstructured":"Esteves, C., Allen-Blanchette, C., Makadia, A., Daniilidis, K.: Learning SO(3) equivariant representations with spherical CNNs. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 54\u201370. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_4"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Feng, B.Y., Yao, W., Liu, Z., Varshney, A.: Deep depth estimation on 360$$^{\\circ }$$ images with a double quaternion loss. In: 3DV (2020)","DOI":"10.1109\/3DV50981.2020.00062"},{"issue":"2","key":"32_CR25","first-page":"1255","volume":"5","author":"C Fernandez-Labrador","year":"2020","unstructured":"Fernandez-Labrador, C., Facil, J.M., Perez-Yus, A., Demonceaux, C., Civera, J., Guerrero, J.J.: Corners for layout: end-to-end layout recovery from 360 images. RA-L 5(2), 1255\u20131262 (2020)","journal-title":"RA-L"},{"issue":"12","key":"32_CR26","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1364\/JOSAA.4.002379","volume":"4","author":"DJ Field","year":"1987","unstructured":"Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. Josa a 4(12), 2379\u20132394 (1987)","journal-title":"Josa a"},{"issue":"3","key":"32_CR27","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/s11263-012-0607-7","volume":"104","author":"R Garg","year":"2013","unstructured":"Garg, R., Roussos, A., Agapito, L.: A variational approach to video registration with subspace constraints. IJCV 104(3), 286\u2013314 (2013)","journal-title":"IJCV"},{"issue":"11","key":"32_CR28","first-page":"1231","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. IJRR 32(11), 1231\u20131237 (2013)","journal-title":"IJRR"},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"32_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/3-540-45053-X_29","volume-title":"Computer Vision \u2014 ECCV 2000","author":"C Geyer","year":"2000","unstructured":"Geyer, C., Daniilidis, K.: A unifying theory for central panoramic systems and practical implications. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 445\u2013461. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45053-X_29"},{"key":"32_CR31","unstructured":"Goralczyk, A.: Nishita sky demo (2020), creative Commons CC0 (Public Domain) - Blender Studio - cloud.blender.org"},{"issue":"1\u20133","key":"32_CR32","first-page":"185","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B.K., Schunck, B.G.: Determining optical flow. AI 17(1\u20133), 185\u2013203 (1981)","journal-title":"AI"},{"key":"32_CR33","unstructured":"Horn, B., Schunck, B.: Techniques and applications of image understanding (1981)"},{"key":"32_CR34","doi-asserted-by":"crossref","unstructured":"Hui, T.W., Tang, X., Loy, C.C.: LiteFlowNet: a lightweight convolutional neural network for optical flow estimation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00936"},{"issue":"8","key":"32_CR35","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1109\/TPAMI.2020.2976928","volume":"43","author":"TW Hui","year":"2021","unstructured":"Hui, T.W., Tang, X., Loy, C.C.: A lightweight optical flow CNN -revisiting data fidelity and regularization. TPAMI 43(8), 2555\u20132569 (2021)","journal-title":"TPAMI"},{"key":"32_CR36","unstructured":"Hulle, S.V.: Bcon19 (2019), 2019 Blender Conference - cloud.blender.org"},{"key":"32_CR37","doi-asserted-by":"crossref","unstructured":"Hur, J., Roth, S.: MirrorFlow: exploiting symmetries in joint optical flow and occlusion estimation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.42"},{"key":"32_CR38","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: CVPR (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"32_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-49409-8_1","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"JJ Yu","year":"2016","unstructured":"Yu, J.J., Harley, A.W., Derpanis, K.G.: Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 3\u201310. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_1"},{"key":"32_CR40","doi-asserted-by":"crossref","unstructured":"Jiang, S., Campbell, D., Lu, Y., Li, H., Hartley, R.: Learning to estimate hidden motions with global motion aggregation. arXiv (2021)","DOI":"10.1109\/ICCV48922.2021.00963"},{"key":"32_CR41","doi-asserted-by":"crossref","unstructured":"Liu, C., Freeman, W.T., Adelson, E.H., Weiss, Y.: Human-assisted motion annotation. In: CVPR (2008)","DOI":"10.1109\/CVPR.2008.4587845"},{"key":"32_CR42","doi-asserted-by":"crossref","unstructured":"Liu, P., Lyu, M., King, I., Xu, J.: SelFlow: self-supervised learning of optical flow. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00470"},{"key":"32_CR43","unstructured":"Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, vol. 2 (1981)"},{"key":"32_CR44","doi-asserted-by":"crossref","unstructured":"McCane, B., Novins, K., Crannitch, D., Galvin, B.: On benchmarking optical flow. CVIU 84(1) (2001)","DOI":"10.1006\/cviu.2001.0930"},{"key":"32_CR45","doi-asserted-by":"crossref","unstructured":"Meister, S., Hur, J., Roth, S.: Unflow: unsupervised learning of optical flow with a bidirectional census loss. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12276"},{"issue":"2","key":"32_CR46","doi-asserted-by":"publisher","DOI":"10.1117\/1.OE.51.2.021107","volume":"51","author":"S Meister","year":"2012","unstructured":"Meister, S., J\u00e4hne, B., Kondermann, D.: Outdoor stereo camera system for the generation of real-world benchmark data sets. Opt. Eng. 51(2), 021107 (2012)","journal-title":"Opt. Eng."},{"key":"32_CR47","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"32_CR48","doi-asserted-by":"crossref","unstructured":"Menze, M., Heipke, C., Geiger, A.: Discrete optimization for optical flow. In: GCPR (2015)","DOI":"10.1007\/978-3-319-24947-6_2"},{"key":"32_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/3-540-57956-7_5","volume-title":"Computer Vision \u2014 ECCV \u201994","author":"M Otte","year":"1994","unstructured":"Otte, M., Nagel, H.-H.: Optical flow estimation: advances and comparisons. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 49\u201360. Springer, Heidelberg (1994). https:\/\/doi.org\/10.1007\/3-540-57956-7_5"},{"key":"32_CR50","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Black, M.J.: Optical flow estimation using a spatial pyramid network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.291"},{"key":"32_CR51","doi-asserted-by":"crossref","unstructured":"Seidel, R., Apitzsch, A., Hirtz, G.: OmniFlow: human omnidirectional optical flow. In: CVPR (2021)","DOI":"10.1109\/CVPRW53098.2021.00407"},{"key":"32_CR52","doi-asserted-by":"crossref","unstructured":"Shakernia, O., Vidal, R., Sastry, S.: Omnidirectional egomotion estimation from back-projection flow. In: CVPRW (2003)","DOI":"10.1109\/CVPRW.2003.10074"},{"issue":"1","key":"32_CR53","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1146\/annurev.neuro.24.1.1193","volume":"24","author":"EP Simoncelli","year":"2001","unstructured":"Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24(1), 1193\u20131216 (2001)","journal-title":"Annu. Rev. Neurosci."},{"key":"32_CR54","unstructured":"Sketchfab. https:\/\/sketchfab.com\/"},{"key":"32_CR55","doi-asserted-by":"crossref","unstructured":"Steinbr\u00fccker, F., Pock, T., Cremers, D.: Large displacement optical flow computation without warping. In: ICCV (2009)","DOI":"10.1109\/ICCV.2009.5459364"},{"key":"32_CR56","unstructured":"Su, Y.C., Grauman, K.: Learning spherical convolution for fast features from 360$$^{\\circ }$$ imagery. In: NeurIPS (2017)"},{"key":"32_CR57","doi-asserted-by":"crossref","unstructured":"Su, Y.C., Grauman, K.: Kernel transformer networks for compact spherical convolution. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00967"},{"issue":"6","key":"32_CR58","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1109\/TPAMI.2019.2894353","volume":"42","author":"D Sun","year":"2019","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Models matter, so does training: an empirical study of CNNs for optical flow estimation. TPAMI 42(6), 1408\u20131423 (2019)","journal-title":"TPAMI"},{"key":"32_CR59","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"32_CR60","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":"32_CR61","doi-asserted-by":"crossref","unstructured":"Teney, D., Hebert, M.: Learning to extract motion from videos in convolutional neural networks. In: ACCV (2016)","DOI":"10.1007\/978-3-319-54193-8_26"},{"key":"32_CR62","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Deep end2end voxel2voxel prediction. In: CVPRW (2016)","DOI":"10.1109\/CVPRW.2016.57"},{"key":"32_CR63","unstructured":"Turbosquid: https:\/\/www.turbosquid.com"},{"key":"32_CR64","doi-asserted-by":"crossref","unstructured":"Wang, R., Geraghty, D., Matzen, K., Szeliski, R., Frahm, J.M.: VPLNet: deep single view normal estimation with vanishing points and lines. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00077"},{"key":"32_CR65","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: DeepFlow: large displacement optical flow with deep matching. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.175"},{"key":"32_CR66","unstructured":"Woli\u0144ski, M.: City - 3d model, sketchfab.com"},{"key":"32_CR67","doi-asserted-by":"crossref","unstructured":"Wulff, J., Black, M.J.: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298607"},{"key":"32_CR68","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1007\/978-3-030-01234-2_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Xu, Y., Yu, J., Gao, S.: Saliency detection in 360$$^\\circ $$ videos. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 504\u2013520. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_30"},{"key":"32_CR69","doi-asserted-by":"crossref","unstructured":"Zhao, S., Sheng, Y., Dong, Y., Chang, E.I., Xu, Y., et al.: MaskFlowNet: asymmetric feature matching with learnable occlusion mask. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00631"},{"key":"32_CR70","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/978-3-030-01231-1_28","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Zioulis","year":"2018","unstructured":"Zioulis, N., Karakottas, A., Zarpalas, D., Daras, P.: OmniDepth: dense depth estimation for\u00a0indoors spherical panoramas. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 453\u2013471. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_28"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20074-8_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:53:26Z","timestamp":1728348806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20074-8_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200731","9783031200748"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20074-8_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"12 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}