{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T14:31:37Z","timestamp":1770733897264,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031586750","type":"print"},{"value":"9783031586767","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58676-7_9","type":"book-chapter","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T03:02:44Z","timestamp":1714100564000},"page":"105-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Domain Adaptation with\u00a0Depth-Based 3D Object Detection in\u00a0CARLA Simulator"],"prefix":"10.1007","author":[{"given":"Miguel","family":"Antunes","sequence":"first","affiliation":[]},{"given":"Luis M.","family":"Bergasa","sequence":"additional","affiliation":[]},{"given":"Santiago","family":"Montiel-Mar\u00edn","sequence":"additional","affiliation":[]},{"given":"Fabio","family":"S\u00e1nchez-Garc\u00eda","sequence":"additional","affiliation":[]},{"given":"Pablo","family":"Pardo-Decimavilla","sequence":"additional","affiliation":[]},{"given":"Pedro","family":"Revenga","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"9_CR1","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1\u201316 (2017)"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: SSD: Single shot multibox detector, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9_CR3","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (Jan. 2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Antunes, M., Bergasa, L.M., et al.: Including transfer learning and synthetic data in a training process of a 2D object detector for autonomous driving. In: ROBOT2022: Fifth Iberian Robotics Conference. Springer International Publish- ING, pp. 465\u2013478 (2023)","DOI":"10.1007\/978-3-031-21062-4_38"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Chabot, F., et al.: Deep MANTA: a coarse-to-fine many-task network for joint 2D and 3D vehicle analysis from monocular image (2017)","DOI":"10.1109\/CVPR.2017.198"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, Z., T\u00f3th, R.: SMOKE: single-stage monocular 3D object detection via keypoint estimation (2020)","DOI":"10.1109\/CVPRW50498.2020.00506"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Rukhovich, D., Vorontsova, A., Konushin, A.: ImVoxelNet: image to voxels projection for monocular and multi-view general-purpose 3D object detection (2021)","DOI":"10.1109\/WACV51458.2022.00133"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Huang, K.C., Wu, T.H., Su, H.T., Hsu, W.H.: MonoDTR: monocular 3D object detection with depth-aware transformer (2022)","DOI":"10.1109\/CVPR52688.2022.00398"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Shen, X., Jia, J.: DSGN: deep stereo geometry network for 3D object detection (2020)","DOI":"10.1109\/CVPR42600.2020.01255"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, B., Hu, R., Liang, M., Urtasun, R.: PLUMENET: efficient 3D object detection from stereo images (2021)","DOI":"10.1109\/IROS51168.2021.9635875"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors (2018)","DOI":"10.3390\/s18103337"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Yin, T., Zhou, X., Kr\u00e4henb\u00fchl, P.: Center-based 3D object detection and tracking. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Del Egido, j., et al.: 360 real-time 3D multi-object detection and tracking for autonomous vehicle navigation. In: Advances in Physical Agents II. Springer International Publishing, pp. 241\u2013255 (2021)","DOI":"10.1007\/978-3-030-62579-5_17"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Zimmer, W., Grabler, M., Knoll, A.: Real-time and robust 3D object detection within road-side lidars using domain adaptation (2023)","DOI":"10.1007\/978-981-19-8361-0_13"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Dworak, D., et al.: Performance of LiDAR object detection deep learning architectures based on artificially generated point cloud data from CARLA simulator. In: MMAR 2019, pp. 600\u2013605(2019)","DOI":"10.1109\/MMAR.2019.8864642"},{"key":"9_CR17","unstructured":"You, Y., Wang, Y., et al.: Pseudo-LiDAR++: accurate depth for 3D object detection in autonomous driving (2020)"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Bangunharcana, A., Cho, J.W., et al.: Correlate-and-Excite: real-time stereo matching via guided cost volume excitation (2021)","DOI":"10.1109\/IROS51168.2021.9635909"},{"key":"9_CR19","unstructured":"Tankovich, V., H\u00e4ne, C., Zhang, Y., Kowdle, A., Fanello, S., Bouaziz, S.: HITNet: Hierarchical iterative tile refinement network for real-time stereo matching (2023)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Li, Z., Chen, Z., Liu, X., Jiang, J.: DepthFormer: exploiting long-range correlation and local information for accurate monocular depth estimation (2022)","DOI":"10.1007\/s11633-023-1458-0"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Yuan, W., Gu, X., Dai, Z., Zhu, S., Tan, P.: New CRFs: neural window fully- connected CRFS for monocular depth estimation (2022)","DOI":"10.1109\/CVPR52688.2022.00389"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Sun, T., Segu, M., et al.: SHIFT: a synthetic driving dataset for continuous multi- task domain adaptation. In: CVPR, pp. 21371\u201321382 (2022)","DOI":"10.1109\/CVPR52688.2022.02068"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"24. 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"}],"container-title":["Lecture Notes in Networks and Systems","Robot 2023: Sixth Iberian Robotics Conference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58676-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T18:03:28Z","timestamp":1770660208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58676-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031586750","9783031586767"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58676-7_9","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ROBOT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Robotics conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"robot2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iberianroboticsconf.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}