{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:35:31Z","timestamp":1778081731483,"version":"3.51.4"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198113","type":"print"},{"value":"9783031198120","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\/978-3-031-19812-0_19","type":"book-chapter","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T14:03:42Z","timestamp":1667052222000},"page":"322-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MonoPLFlowNet: Permutohedral Lattice FlowNet for\u00a0Real-Scale 3D Scene Flow Estimation with\u00a0Monocular Images"],"prefix":"10.1007","author":[{"given":"Runfa","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Truong","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Adams, A., Baek, J., Davis, M.A.: Fast high-dimensional filtering using the permutohedral lattice. Comput. Graph. Forum 29 (2010)","DOI":"10.1111\/j.1467-8659.2009.01645.x"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Behl, A., Jafari, O.H., Mustikovela, S.K., Alhaija, H.A., Rother, C., Geiger, A.: Bounding boxes, segmentations and object coordinates: how important is recognition for 3d scene flow estimation in autonomous driving scenarios? In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2593\u20132602 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.281","DOI":"10.1109\/ICCV.2017.281"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Behl, A., Paschalidou, D., Donn\u00e9, S., Geiger, A.: PointFlowNet: learning representations for rigid motion estimation from point clouds. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7954\u20137963 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00815","DOI":"10.1109\/CVPR.2019.00815"},{"issue":"2","key":"19_CR4","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"P Besl","year":"1992","unstructured":"Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239\u2013256 (1992). https:\/\/doi.org\/10.1109\/34.121791","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"19_CR5","doi-asserted-by":"publisher","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. 33(3), 500\u2013513 (2011). https:\/\/doi.org\/10.1109\/TPAMI.2010.143","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77\u201385 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.16","DOI":"10.1109\/CVPR.2017.16"},{"issue":"4","key":"19_CR7","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Chen, Y., Schmid, C., Sminchisescu, C.: Self-supervised learning with geometric constraints in monocular video: connecting flow, depth, and camera. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7062\u20137071 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00716","DOI":"10.1109\/ICCV.2019.00716"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2758\u20132766 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.316","DOI":"10.1109\/ICCV.2015.316"},{"key":"19_CR10","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014). https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/7bccfde7714a1ebadf06c5f4cea752c1-Paper.pdf"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.: Deep ordinal regression network for monocular depth estimation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2002\u20132011 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00214","DOI":"10.1109\/CVPR.2018.00214"},{"key":"19_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-030-01219-9_14","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Gan","year":"2018","unstructured":"Gan, Y., Xu, X., Sun, W., Lin, L.: Monocular depth estimation with affinity, vertical pooling, and label enhancement. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 232\u2013247. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_14"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"19_CR14","doi-asserted-by":"publisher","unstructured":"Godard, C., Aodha, O.M., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6602\u20136611 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.699","DOI":"10.1109\/CVPR.2017.699"},{"key":"19_CR15","doi-asserted-by":"publisher","unstructured":"Gu, X., Wang, Y., Wu, C., Lee, Y.J., Wang, P.: HPLFlowNet: hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3249\u20133258 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00337","DOI":"10.1109\/CVPR.2019.00337"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Horn\u00e1cek, M., Fitzgibbon, A., Rother, C.: SphereFlow: 6 DoF scene flow from RGB-D pairs. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3526\u20133533 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.451","DOI":"10.1109\/CVPR.2014.451"},{"key":"19_CR17","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Hur, J., Roth, S.: Self-supervised monocular scene flow estimation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7394\u20137403 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00742","DOI":"10.1109\/CVPR42600.2020.00742"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Hur, J., Roth, S.: Self-supervised multi-frame monocular scene flow. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2684\u20132694, June 2021","DOI":"10.1109\/CVPR46437.2021.00271"},{"key":"19_CR20","doi-asserted-by":"publisher","unstructured":"Jaimez, M., Souiai, M., St\u00fcckler, J., Gonzalez-Jimenez, J., Cremers, D.: Motion cooperation: smooth piece-wise rigid scene flow from RGB-D images. In: 2015 International Conference on 3D Vision, pp. 64\u201372 (2015). https:\/\/doi.org\/10.1109\/3DV.2015.15","DOI":"10.1109\/3DV.2015.15"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Jampani, V., Kiefel, M., Gehler, P.V.: Learning sparse high dimensional filters: image filtering, dense CRFs and bilateral neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4452\u20134461 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.482","DOI":"10.1109\/CVPR.2016.482"},{"key":"19_CR22","doi-asserted-by":"publisher","unstructured":"Jiang, H., Sun, D., Jampani, V., Lv, Z., Learned-Miller, E., Kautz, J.: Sense: a shared encoder network for scene-flow estimation. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3194\u20133203 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00329","DOI":"10.1109\/ICCV.2019.00329"},{"key":"19_CR23","doi-asserted-by":"publisher","unstructured":"Kuznietsov, Y., St\u00fcckler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2215\u20132223 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.238","DOI":"10.1109\/CVPR.2017.238"},{"key":"19_CR24","unstructured":"Lee, J.H., Han, M.K., Ko, D.W., Suh, I.H.: From big to small: multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326 (2019)"},{"key":"19_CR25","doi-asserted-by":"publisher","unstructured":"Li, R., Nguyen, T.: SM3D: simultaneous monocular mapping and 3D detection. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 3652\u20133656 (2021). https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506302","DOI":"10.1109\/ICIP42928.2021.9506302"},{"issue":"10","key":"19_CR26","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1109\/TPAMI.2015.2505283","volume":"38","author":"F Liu","year":"2016","unstructured":"Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2024\u20132039 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2505283","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR27","doi-asserted-by":"publisher","unstructured":"Liu, X., Qi, C.R., Guibas, L.J.: FlowNet3D: learning scene flow in 3D point clouds. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 529\u2013537 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00062","DOI":"10.1109\/CVPR.2019.00062"},{"issue":"10","key":"19_CR28","doi-asserted-by":"publisher","first-page":"2624","DOI":"10.1109\/TPAMI.2019.2930258","volume":"42","author":"C Luo","year":"2020","unstructured":"Luo, C., et al.: Every pixel counts ++: joint learning of geometry and motion with 3d holistic understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2624\u20132641 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2930258","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/978-3-030-01228-1_29","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Lv","year":"2018","unstructured":"Lv, Z., Kim, K., Troccoli, A., Sun, D., Rehg, J.M., Kautz, J.: Learning rigidity in dynamic scenes with a moving camera for 3D motion field estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 484\u2013501. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_29"},{"key":"19_CR30","doi-asserted-by":"publisher","unstructured":"Ma, W.C., Wang, S., Hu, R., Xiong, Y., Urtasun, R.: Deep rigid instance scene flow. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3609\u20133617 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00373","DOI":"10.1109\/CVPR.2019.00373"},{"key":"19_CR31","doi-asserted-by":"publisher","unstructured":"Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4040\u20134048 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.438","DOI":"10.1109\/CVPR.2016.438"},{"key":"19_CR32","doi-asserted-by":"publisher","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3061\u20133070 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298925","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"19_CR33","doi-asserted-by":"publisher","unstructured":"Mittal, H., Okorn, B., Held, D.: Just go with the flow: self-supervised scene flow estimation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11174\u201311182 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01119","DOI":"10.1109\/CVPR42600.2020.01119"},{"issue":"12","key":"19_CR34","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","volume":"32","author":"A Myronenko","year":"2010","unstructured":"Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262\u20132275 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2010.46","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR35","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS (2017)"},{"key":"19_CR36","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) (2020)"},{"key":"19_CR37","doi-asserted-by":"publisher","unstructured":"Ranjan, A., et al.: Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12232\u201312241 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.01252","DOI":"10.1109\/CVPR.2019.01252"},{"issue":"5","key":"19_CR38","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TPAMI.2008.132","volume":"31","author":"A Saxena","year":"2009","unstructured":"Saxena, A., Sun, M., Ng, A.Y.: MAKE3D: learning 3D scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824\u2013840 (2009). https:\/\/doi.org\/10.1109\/TPAMI.2008.132","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR39","doi-asserted-by":"publisher","unstructured":"Schuster, R., Wasenmuller, O., Kuschk, G., Bailer, C., Stricker, D.: SceneFlowFields: dense interpolation of sparse scene flow correspondences. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1056\u20131065 (2018). https:\/\/doi.org\/10.1109\/WACV.2018.00121","DOI":"10.1109\/WACV.2018.00121"},{"key":"19_CR40","doi-asserted-by":"publisher","unstructured":"Su, H., et al.: SplatNet: sparse lattice networks for point cloud processing. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2530\u20132539 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00268","DOI":"10.1109\/CVPR.2018.00268"},{"key":"19_CR41","doi-asserted-by":"publisher","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00931","DOI":"10.1109\/CVPR.2018.00931"},{"key":"19_CR42","doi-asserted-by":"publisher","unstructured":"Taniai, T., Sinha, S.N., Sato, Y.: Fast multi-frame stereo scene flow with motion segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6891\u20136900 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.729","DOI":"10.1109\/CVPR.2017.729"},{"issue":"1","key":"19_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-015-0806-0","volume":"115","author":"C Vogel","year":"2015","unstructured":"Vogel, C., Schindler, K., Roth, S.: 3D scene flow estimation with a piecewise rigid scene model. Int. J. Comput. Vision 115(1), 1\u201328 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"19_CR44","doi-asserted-by":"publisher","unstructured":"Wang, Z., Li, S., Howard-Jenkins, H., Prisacariu, V.A., Chen, M.: Flownet3d++: geometric losses for deep scene flow estimation. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 91\u201398 (2020). https:\/\/doi.org\/10.1109\/WACV45572.2020.9093302","DOI":"10.1109\/WACV45572.2020.9093302"},{"key":"19_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-030-33676-9_24","volume-title":"Pattern Recognition","author":"AS Wannenwetsch","year":"2019","unstructured":"Wannenwetsch, A.S., Kiefel, M., Gehler, P.V., Roth, S.: Learning task-specific generalized convolutions in the permutohedral lattice. In: Fink, G.A., Frintrop, S., Jiang, X. (eds.) DAGM GCPR 2019. LNCS, vol. 11824, pp. 345\u2013359. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33676-9_24"},{"key":"19_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/978-3-030-58558-7_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"W Wu","year":"2020","unstructured":"Wu, W., Wang, Z.Y., Li, Z., Liu, W., Fuxin, L.: PointPWC-net: cost volume on point clouds for (self-)supervised scene flow estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 88\u2013107. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_6"},{"key":"19_CR47","doi-asserted-by":"publisher","unstructured":"Xu, Y., Wang, Y., Guo, L.: Unsupervised ego-motion and dense depth estimation with monocular video. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 1306\u20131310 (2018). https:\/\/doi.org\/10.1109\/ICCT.2018.8600039","DOI":"10.1109\/ICCT.2018.8600039"},{"key":"19_CR48","doi-asserted-by":"publisher","unstructured":"Yang, G., Ramanan, D.: Upgrading optical flow to 3D scene flow through optical expansion. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1331\u20131340 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00141","DOI":"10.1109\/CVPR42600.2020.00141"},{"key":"19_CR49","doi-asserted-by":"publisher","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3684\u20133692 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00388","DOI":"10.1109\/CVPR.2018.00388"},{"key":"19_CR50","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, P., Wang, Y., Xu, W., Nevatia, R.: Every pixel counts: unsupervised geometry learning with holistic 3D motion understanding (2018)","DOI":"10.1007\/978-3-030-11021-5_43"},{"key":"19_CR51","doi-asserted-by":"publisher","unstructured":"Yin, W., Liu, Y., Shen, C., Yan, Y.: Enforcing geometric constraints of virtual normal for depth prediction. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 5683\u20135692 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00578","DOI":"10.1109\/ICCV.2019.00578"},{"key":"19_CR52","doi-asserted-by":"publisher","unstructured":"Yin, Z., Shi, J.: GeoNet: unsupervised learning of dense depth, optical flow and camera pose. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1983\u20131992 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00212","DOI":"10.1109\/CVPR.2018.00212"},{"key":"19_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1007\/978-3-319-46475-6_47","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Q-Y Zhou","year":"2016","unstructured":"Zhou, Q.-Y., Park, J., Koltun, V.: Fast global registration. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 766\u2013782. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_47"},{"key":"19_CR54","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-030-01228-1_3","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zou","year":"2018","unstructured":"Zou, Y., Luo, Z., Huang, J.-B.: DF-net: unsupervised joint learning of depth and flow using cross-task consistency. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 38\u201355. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_3"}],"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\/978-3-031-19812-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T13:06:46Z","timestamp":1682600806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19812-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198113","9783031198120"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19812-0_19","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":"30 October 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)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}