{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:49:07Z","timestamp":1782863347911,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3009601"],"award-info":[{"award-number":["2023YFC3009601"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971017"],"award-info":[{"award-number":["71971017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s00138-026-01844-7","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:03:16Z","timestamp":1780585396000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An unsupervised learning framework for optical flow prediction in odometry with exploitable depth information"],"prefix":"10.1007","volume":"37","author":[{"given":"Shengke","family":"Niu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongjia","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"1844_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Singh, S.: Visual-lidar odometry and mapping: low-drift, robust, and fast. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). pp. 2174\u20132181. IEEE, Seattle, WA, USA (2015)","DOI":"10.1109\/ICRA.2015.7139486"},{"key":"1844_CR2","doi-asserted-by":"crossref","unstructured":"Abouee, A., Ravi, A., Hinneburg, L., Dziwulski, M., \u00d6lsner, F., Hess, J., Milz, S., M\u00e4der, P.: Weakly Supervised End2End Deep Visual Odometry. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 858\u2013865. IEEE, Seattle, WA, USA (2024)","DOI":"10.1109\/CVPRW63382.2024.00091"},{"key":"1844_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126813","volume":"274","author":"L Cao","year":"2025","unstructured":"Cao, L., Liu, J., Lei, J., Zhang, W., Chen, Y., Hyypp\u00e4, J.: Real-time motion state estimation of feature points based on optical flow field for robust monocular visual-inertial odometry in dynamic scenes. Expert Syst. Appl. 274, 126813 (2025). https:\/\/doi.org\/10.1016\/j.eswa.2025.126813","journal-title":"Expert Syst. Appl."},{"key":"1844_CR4","unstructured":"Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision (IJCAI). Presented at the [No source information available] (1981)"},{"key":"1844_CR5","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","volume":"34","author":"T Qin","year":"2018","unstructured":"Qin, T., Li, P., Shen, S.: VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans. Robot. 34, 1004\u20131020 (2018). https:\/\/doi.org\/10.1109\/TRO.2018.2853729","journal-title":"IEEE Trans. Robot."},{"key":"1844_CR6","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","volume":"37","author":"C Campos","year":"2021","unstructured":"Campos, C., Elvira, R., Gomez Rodriguez, J.J., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Trans. Robot. 37, 1874\u20131890 (2021). https:\/\/doi.org\/10.1109\/TRO.2021.3075644","journal-title":"IEEE Trans. Robot."},{"key":"1844_CR7","doi-asserted-by":"crossref","unstructured":"Zhao, S., Sheng, Y., Dong, Y., Chang, E.I.-C., Xu, Y.: MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6277\u20136286. IEEE Computer Soc, Los Alamitos (2020)","DOI":"10.1109\/CVPR42600.2020.00631"},{"key":"1844_CR8","doi-asserted-by":"crossref","unstructured":"Bailer, C., Varanasi, K., Stricker, D.: CNN-based patch matching for optical flow with thresholded hinge embedding loss. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). pp. 2710\u20132719. IEEE, New York (2017)","DOI":"10.1109\/CVPR.2017.290"},{"key":"1844_CR9","doi-asserted-by":"crossref","unstructured":"Xu, J., Ranftl, R., Koltun, V.: Accurate optical flow via direct cost volume processing. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). pp. 5807\u20135815. IEEE, New York (2017)","DOI":"10.1109\/CVPR.2017.615"},{"key":"1844_CR10","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Haeusser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: FlowNet: Learning optical flow with convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 2758\u20132766. IEEE, New York (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"1844_CR11","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: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). pp. 1647\u20131655. IEEE, New York (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"1844_CR12","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Black, M.J.: Optical flow estimation using a spatial pyramid network. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). pp. 2720\u20132729. IEEE, New York (2017)","DOI":"10.1109\/CVPR.2017.291"},{"key":"1844_CR13","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: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8934\u20138943. IEEE, New York (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"1844_CR14","doi-asserted-by":"crossref","unstructured":"Hui, T.-W., Tang, X., Loy, C.C.: LiteFlowNet: A lightweight convolutional neural network for optical flow estimation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8981\u20138989. IEEE, New York (2018)","DOI":"10.1109\/CVPR.2018.00936"},{"key":"1844_CR15","doi-asserted-by":"crossref","unstructured":"Hur, J., Roth, S.: Iterative Residual refinement for joint optical flow and occlusion estimation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). pp. 5747\u20135756. IEEE Computer Soc, Los Alamitos (2019)","DOI":"10.1109\/CVPR.2019.00590"},{"key":"1844_CR16","doi-asserted-by":"crossref","unstructured":"Liu, H., Lu, T., Xu, Y., Liu, J., Li, W., Chen, L.: CamLiFlow: bidirectional camera-LiDAR fusion for joint optical flow and scene flow estimation. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). pp. 5781\u20135791. IEEE Computer Soc, Los Alamitos (2022)","DOI":"10.1109\/CVPR52688.2022.00570"},{"key":"1844_CR17","first-page":"4839","volume-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021","author":"Z Teed","year":"2021","unstructured":"Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow (extended abstract). In: Zhou, Z.H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, pp. 4839\u20134843. Ijcai-Int Joint Conf Artif Intell, Freiburg (2021)"},{"key":"1844_CR18","doi-asserted-by":"crossref","unstructured":"Meister, S., Hur, J., Roth, S.: UnFlow: unsupervised learning of optical flow with a bidirectional census loss. http:\/\/arxiv.org\/abs\/1711.07837 (2017)","DOI":"10.1609\/aaai.v32i1.12276"},{"key":"1844_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Y., Yang, Z., Zhao, L., Wang, P., Xu, W.: Occlusion aware unsupervised learning of optical flow. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4884\u20134893. IEEE, Salt Lake City, UT (2018)","DOI":"10.1109\/CVPR.2018.00513"},{"key":"1844_CR20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018770","author":"P Liu","year":"2019","unstructured":"Liu, P., King, I., Lyu, M.R., Xu, J.: DDFlow: learning optical flow with unlabeled data distillation. Proc. AAAI Conf. Artif. Intell. (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33018770","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1844_CR21","doi-asserted-by":"crossref","unstructured":"Liu, P., Lyu, M., King, I., Xu, J.: SelFlow: self-supervised learning of optical flow. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). pp. 4566\u20134575. IEEE Computer Soc, Los Alamitos (2019)","DOI":"10.1109\/CVPR.2019.00470"},{"key":"1844_CR22","doi-asserted-by":"publisher","first-page":"9113","DOI":"10.1109\/TIP.2020.3024015","volume":"29","author":"Z Ren","year":"2020","unstructured":"Ren, Z., Luo, W., Yan, J., Liao, W., Yang, X., Yuille, A., Zha, H.: STFlow: self-taught optical flow estimation using pseudo labels. IEEE Trans. Image Process. 29, 9113\u20139124 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.3024015","journal-title":"IEEE Trans. Image Process."},{"key":"1844_CR23","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/978-3-030-01270-0_42","volume-title":"Computer Vision - ECCV 2018, PT XVI","author":"J Janai","year":"2018","unstructured":"Janai, J., Guney, F., Ranjan, A., Black, M., Geiger, A.: Unsupervised learning of multi-frame optical flow with occlusions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018, PT XVI, pp. 713\u2013731. Springer International Publishing Ag, Cham (2018)"},{"key":"1844_CR24","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Ji, P., Wang, J., Dai, Y., Li, H.: Unsupervised deep epipolar flow for stationary or dynamic scenes. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). pp. 12087\u201312096. IEEE, New York (2019)","DOI":"10.1109\/CVPR.2019.01237"},{"key":"1844_CR25","doi-asserted-by":"crossref","unstructured":"Guo, S., Hamann, F., Gallego, G.: Unsupervised joint learning of optical flow and intensity with event cameras. http:\/\/arxiv.org\/abs\/2503.17262 (2025)","DOI":"10.1109\/ICCV51701.2025.00748"},{"key":"1844_CR26","doi-asserted-by":"crossref","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 (CVPR). pp. 1983\u20131992. IEEE, New York (2018)","DOI":"10.1109\/CVPR.2018.00212"},{"key":"1844_CR27","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-030-01228-1_3","volume-title":"Computer Vision - ECCV 2018, PT V","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.) Computer Vision - ECCV 2018, PT V, pp. 38\u201355. Springer International Publishing Ag, Cham (2018)"},{"key":"1844_CR28","doi-asserted-by":"crossref","unstructured":"Luo, K., Wang, C., Liu, S., Fan, H., Wang, J., Sun, J.: UPFlow: upsampling pyramid for unsupervised optical flow learning. http:\/\/arxiv.org\/abs\/2012.00212 (2021)","DOI":"10.1109\/CVPR46437.2021.00110"},{"key":"1844_CR29","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"1844_CR30","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/BFb0028345","volume-title":"Computer Vision: ECCV \u201994","author":"R Zabih","year":"1994","unstructured":"Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) Computer Vision: ECCV \u201994, pp. 151\u2013158. Springer Berlin Heidelberg, Berlin, Heidelberg (1994)"},{"key":"1844_CR31","first-page":"438","volume-title":"Computer Vision: ECCV 2010","author":"D Hutchison","year":"2010","unstructured":"Hutchison, D., Kanade, T., Kittler, J., Kleinberg, J.M., Mattern, F., Mitchell, J.C., Naor, M., Nierstrasz, O., Pandu Rangan, C., Steffen, B., Sudan, M., Terzopoulos, D., Tygar, D., Vardi, M.Y., Weikum, G., Sundaram, N., Brox, T., Keutzer, K.: Dense point trajectories by GPU-accelerated large displacement optical flow. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision: ECCV 2010, pp. 438\u2013451. Springer Berlin Heidelberg, Berlin, Heidelberg (2010)"},{"key":"1844_CR32","unstructured":"Luo, K., Wang, C., Ye, N., Liu, S., Wang, J.: OccInpFlow: occlusion-inpainting optical flow estimation by unsupervised learning (2020)"},{"key":"1844_CR33","unstructured":"Cleveston, I., Colombini, E.L.: RAM-VO: less is more in visual odometry. http:\/\/arxiv.org\/abs\/2107.02974 (2021)"},{"key":"1844_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. http:\/\/arxiv.org\/abs\/1704.07813 (2017)","DOI":"10.1109\/CVPR.2017.700"},{"key":"1844_CR35","doi-asserted-by":"crossref","unstructured":"Nubert, J., Khattak, S., Hutter, M.: Self-supervised learning of LiDAR odometry for robotic applications. http:\/\/arxiv.org\/abs\/2011.05418 (2021)","DOI":"10.1109\/ICRA48506.2021.9561063"},{"key":"1844_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, J., Singh, S.: LOAM: Lidar odometry and mapping in real-time. In: Robotics: Science and Systems X. Robotics: Science and Systems Foundation (2014)","DOI":"10.15607\/RSS.2014.X.007"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-026-01844-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-026-01844-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-026-01844-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T22:54:48Z","timestamp":1782860088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-026-01844-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,4]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["1844"],"URL":"https:\/\/doi.org\/10.1007\/s00138-026-01844-7","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,4]]},"assertion":[{"value":"1 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"84"}}