{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T19:32:48Z","timestamp":1789414368344,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032370372","type":"print"},{"value":"9783032370389","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-37038-9_27","type":"book-chapter","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:34:17Z","timestamp":1789122857000},"page":"483-501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["UniFlow: Zero-Shot LiDAR Scene Flow for\u00a0Autonomous Driving"],"prefix":"10.1007","author":[{"given":"Siyi","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ishan","family":"Khatri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle","family":"Vedder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Eaton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deva","family":"Ramanan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neehar","family":"Peri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuscenes: a multimodal dataset for autonomous driving. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Caunes, A., Chateau, T., Fremont, V.: seg_3d_by_pc2d: multi-view projection for domain generalization and adaptation in 3d semantic segmentation. arXiv preprint arXiv:2505.15545 (2025)","DOI":"10.1007\/978-3-032-31452-9_4"},{"key":"27_CR3","doi-asserted-by":"publisher","unstructured":"Cen, J., et al.: Open-world semantic segmentation for lidar point clouds. In: European Conference on Computer Vision, pp. 318\u2013334. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-19839-7_19","DOI":"10.1007\/978-3-031-19839-7_19"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Chodosh, N., Ramanan, D., Lucey, S.: Re-evaluating lidar scene flow. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6005\u20136015 (2024)","DOI":"10.1109\/WACV57701.2024.00590"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"62062","DOI":"10.52202\/079017-1982","volume":"37","author":"F Fent","year":"2024","unstructured":"Fent, F., et al.: Man truckscenes: a multimodal dataset for autonomous trucking in diverse conditions. Adv. Neural. Inf. Process. Syst. 37, 62062\u201362082 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Hegde, D., Lohit, S., Peng, K.C., Jones, M., Patel, V.: Multimodal 3d object detection on unseen domains. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 2499\u20132509 (2025)","DOI":"10.1109\/CVPRW67362.2025.00235"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Himmelsbach, M., Hundelshausen, F.V., Wuensche, H.J.: Fast segmentation of 3d point clouds for ground vehicles. In: Intelligent Vehicles Symposium (IV), pp. 560\u2013565. IEEE (2010)","DOI":"10.1109\/IVS.2010.5548059"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Hoffmann, D.T., Raza, S.H., Jiang, H., Tananaev, D., Klingenhoefer, S., Meinke, M.: Floxels: fast unsupervised voxel based scene flow estimation. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 22328\u201322337 (2025)","DOI":"10.1109\/CVPR52734.2025.02080"},{"key":"27_CR10","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"},{"issue":"2","key":"27_CR11","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/LRA.2021.3139542","volume":"7","author":"P Jund","year":"2021","unstructured":"Jund, P., Sweeney, C., Abdo, N., Chen, Z., Shlens, J.: Scalable scene flow from point clouds in the real world. IEEE Robot. Autom. Lett. 7(2), 1589\u20131596 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"27_CR12","doi-asserted-by":"publisher","unstructured":"Khatri, I., Vedder, K., Peri, N., Ramanan, D., Hays, J.: I can\u2019t believe it\u2019s not scene flow! In: European Conference on Computer Vision, pp. 242\u2013257. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-72649-1_14","DOI":"10.1007\/978-3-031-72649-1_14"},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Khoche, A., Zhang, Q., Sanchez, L.P., Asefaw, A., Mansouri, S.S., Jensfelt, P.: SSF: sparse long-range scene flow for autonomous driving. In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 6394\u20136400 (2025). https:\/\/doi.org\/10.1109\/ICRA55743.2025.11128770","DOI":"10.1109\/ICRA55743.2025.11128770"},{"key":"27_CR14","doi-asserted-by":"publisher","unstructured":"Kim, J., Woo, J., Kim, J., Im, S.: Rethinking lidar domain generalization: single source as multiple density domains. In: European Conference on Computer Vision, pp. 310\u2013327. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-72661-3_18","DOI":"10.1007\/978-3-031-72661-3_18"},{"key":"27_CR15","doi-asserted-by":"publisher","unstructured":"Kim, J., Woo, J., Shin, U., Oh, J., Im, S.: Flow4D: leveraging 4d voxel network for lidar scene flow estimation. IEEE Robot. Autom. Lett., 1\u20138 (2025). https:\/\/doi.org\/10.1109\/LRA.2025.3542327","DOI":"10.1109\/LRA.2025.3542327"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Lambert, J., Liu, Z., Sener, O., Hays, J., Koltun, V.: Mseg: a composite dataset for multi-domain semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2879\u20132888 (2020)","DOI":"10.1109\/CVPR42600.2020.00295"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Lang, I., Aiger, D., Cole, F., Avidan, S., Rubinstein, M.: Scoop: self-supervised correspondence and optimization-based scene flow. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5281\u20135290 (2023)","DOI":"10.1109\/CVPR52729.2023.00511"},{"key":"27_CR18","first-page":"7838","volume":"34","author":"X Li","year":"2021","unstructured":"Li, X., Kaesemodel Pontes, J., Lucey, S.: Neural scene flow prior. Adv. Neural. Inf. Process. Syst. 34, 7838\u20137851 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Li, X., Zheng, J., Ferroni, F., Pontes, J.K., Lucey, S.: Fast neural scene flow. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9878\u20139890 (2023)","DOI":"10.1109\/ICCV51070.2023.00906"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Liang, Y., Badki, A., Su, H., Tompkin, J., Gallo, O.: Zero-shot monocular scene flow estimation in the wild. In: Proceedings of the Computer Vision and Pattern Recognition Conference (2025)","DOI":"10.1109\/CVPR52734.2025.01959"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Lin, Y., Caesar, H.: Icp-flow: lidar scene flow estimation with icp. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01468"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Lin, Y., Wang, S., Nan, L., Kooij, J., Caesar, H.: Voteflow: enforcing local rigidity in self-supervised scene flow. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 17155\u201317164 (2025)","DOI":"10.1109\/CVPR52734.2025.01599"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Multi-space alignments towards universal lidar segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14648\u201314661 (2024)","DOI":"10.1109\/CVPR52733.2024.01388"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Multi-space alignments towards universal lidar segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14648\u201314661 (June 2024)","DOI":"10.1109\/CVPR52733.2024.01388"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Luo, J., Cheng, J., Tang, X., Zhang, Q., Xue, B., Fan, R.: Mambaflow: a novel and flow-guided state space model for scene flow estimation, arXiv preprint arXiv:2502.16907 (2025)","DOI":"10.1109\/TIV.2026.3663171"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Madan, A., Peri, N., Kong, S., Ramanan, D.: Revisiting few-shot object detection with vision-language models, arXiv preprint arXiv:2312.14494 (2023)","DOI":"10.52202\/079017-0617"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Mali\u0107, D., Fruhwirth-Reisinger, C., Schulter, S., Possegger, H.: Gblobs: explicit local structure via gaussian blobs for improved cross-domain lidar-based 3d object detection. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 27357\u201327367 (2025)","DOI":"10.1109\/CVPR52734.2025.02548"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Michele, B., Boulch, A., Puy, G., Vu, T.H., Marlet, R., Courty, N.: Saluda: surface-based automotive lidar unsupervised domain adaptation. In: 2024 International Conference on 3D Vision (3DV), pp. 421\u2013431. IEEE (2024)","DOI":"10.1109\/3DV62453.2024.00134"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Peri, N., Li, M., Wilson, B., Wang, Y.X., Hays, J., Ramanan, D.: An empirical analysis of range for 3d object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4074\u20134083 (2023)","DOI":"10.1109\/ICCVW60793.2023.00440"},{"key":"27_CR30","unstructured":"Robicheaux, P., et al.: Roboflow100-vl: a multi-domain object detection benchmark for vision-language models, arXiv preprint arXiv:2505.20612 (2025)"},{"key":"27_CR31","doi-asserted-by":"crossref","unstructured":"Saltori, C., Osep, A., Ricci, E., Leal-Taix\u00e9, L.: Walking your lidog: a journey through multiple domains for lidar semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 196\u2013206 (2023)","DOI":"10.1109\/ICCV51070.2023.00025"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Soum-Fontez, L., Deschaud, J.E., Goulette, F.: Mdt3d: Multi-dataset training for lidar 3d object detection generalization. In: 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5765\u20135772. IEEE (2023)","DOI":"10.1109\/IROS55552.2023.10341614"},{"key":"27_CR33","unstructured":"Sun, J., et al.: An empirical study of training state-of-the-art lidar segmentation models, arXiv preprint arXiv:2405.14870 (2024)"},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446\u20132454. (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"27_CR35","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":"27_CR36","unstructured":"Vedder, K., et al.: ZeroFlow: fast Zero Label Scene Flow via Distillation. International Conference on Learning Representations (ICLR) (2024)"},{"key":"27_CR37","unstructured":"Vedder, K., et al.: Neural eulerian scene flow fields. In: The Thirteenth International Conference on Learning Representations (2025). https:\/\/openreview.net\/forum?id=0CieWy9ONY"},{"issue":"3","key":"27_CR38","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1109\/TPAMI.2005.63","volume":"27","author":"S Vedula","year":"2005","unstructured":"Vedula, S., Rander, P., Collins, R., Kanade, T.: Three-dimensional scene flow. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 475\u2013480 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Wang, C., Ma, C., Zhu, M., Yang, X.: Pointaugmenting: cross-modal augmentation for 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11794\u201311803 (2021)","DOI":"10.1109\/CVPR46437.2021.01162"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Flux4d: flow-based unsupervised 4d reconstruction. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025). https:\/\/openreview.net\/forum?id=FeUGQ6AiKR","DOI":"10.52202\/085713-3529"},{"key":"27_CR41","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: Moge-2: accurate monocular geometry with metric scale and sharp details, arXiv preprint arXiv:2507.02546 (2025)","DOI":"10.52202\/085713-1207"},{"key":"27_CR42","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Train in Germany, test in the USA: Making 3d object detectors generalize. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11713\u201311723 (2020)","DOI":"10.1109\/CVPR42600.2020.01173"},{"key":"27_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wei, Y., Rao, Y., Zhou, J., Lu, J.: 3d point-voxel correlation fields for scene flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3294355"},{"key":"27_CR44","doi-asserted-by":"crossref","unstructured":"Wei, Y., Wang, Z., Rao, Y., Lu, J., Zhou, J.: PV-RAFT: point-voxel correlation fields for scene flow estimation of point clouds. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00688"},{"key":"27_CR45","unstructured":"Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J.: Argoverse 2: Next generation datasets for self-driving perception and forecasting. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021, (2021)"},{"key":"27_CR46","unstructured":"Xu, X., et al.: Superflow++: enhanced spatiotemporal consistency for cross-modal data pretraining. arXiv preprint arXiv:2503.19912 (2025)"},{"key":"27_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, Q., et ak.: HiMo: high-speed objects motion compensation in point cloud, arXiv preprint arXiv:2503.00803 (2025)","DOI":"10.1109\/TRO.2025.3619042"},{"key":"27_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Yang, Y., Fang, H., Geng, R., Jensfelt, P.: DeFlow: decoder of scene flow network in autonomous driving. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 2105\u20132111 (2024). https:\/\/doi.org\/10.1109\/ICRA57147.2024.10610278","DOI":"10.1109\/ICRA57147.2024.10610278"},{"key":"27_CR49","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Yang, Y., Li, P., Andersson, O., Jensfelt, P.: SeFlow: a self-supervised scene flow method in autonomous driving. In: European Conference on Computer Vision (ECCV), pp. 353\u2013369. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-73232-4_20","DOI":"10.1007\/978-3-031-73232-4_20"},{"key":"27_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zhu, X., Zhang, Y., Cai, Y., Andersson, O., Jensfelt, P.: DeltaFlow: an efficient multi-frame scene flow estimation method. arXiv preprint arXiv:2508.17054 (2025)","DOI":"10.52202\/085713-2975"},{"key":"27_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Edstedt, J., Wandt, B., Forss\u00e9n, P.E., Magnusson, M., Felsberg, M.: Gmsf: global matching scene flow. Adv. Neural. Inf. Process. Syst. 36 (2024)","DOI":"10.52202\/075280-2812"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37038-9_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:34:30Z","timestamp":1789122870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37038-9_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032370372","9783032370389"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37038-9_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 September 2026","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":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}