{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T13:10:08Z","timestamp":1748092208975,"version":"3.41.0"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031915680","type":"print"},{"value":"9783031915697","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-91569-7_6","type":"book-chapter","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:49:38Z","timestamp":1748090978000},"page":"73-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FruitBin: A\u00a0Tunable Large-Scale Dataset for\u00a0Advancing 6D Pose Estimation in\u00a0Fruit Bin-Picking Automation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5799-2787","authenticated-orcid":false,"given":"Guillaume","family":"Duret","sequence":"first","affiliation":[]},{"given":"Mahmoud","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Nicolas","family":"Cazin","sequence":"additional","affiliation":[]},{"given":"Danylo","family":"Mazurak","sequence":"additional","affiliation":[]},{"given":"Anna","family":"Samsonenko","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Chapin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-7204","authenticated-orcid":false,"given":"Florence","family":"Zara","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7346-228X","authenticated-orcid":false,"given":"Emmanuel","family":"Dellandrea","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3654-9498","authenticated-orcid":false,"given":"Liming","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Peters","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"6_CR1","unstructured":"Background difusion model. https:\/\/www.promeai.com\/background-diffusion. Accessed 25 Aug 2023"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: Learning 6D Object Pose Estimation Using 3D Object Coordinates. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 536\u2013551. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10605-2_35","DOI":"10.1007\/978-3-319-10605-2_35"},{"key":"6_CR3","unstructured":"Chen, X., Hu, J., Jin, C., Li, L., Wang, L.: Understanding domain randomization for sim-to-real transfer. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=T8vZHIRTrY"},{"key":"6_CR4","unstructured":"Coleman, D., Sucan, I.A., Chitta, S., Correll, N.: Reducing the barrier to entry of complex robotic software: a moveit! case study. ArXiv abs\/1404.3785 (2014)"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"51416","DOI":"10.1109\/ACCESS.2021.3068769","volume":"9","author":"J Collins","year":"2021","unstructured":"Collins, J., Chand, S., Vanderkop, A., Howard, D.: A review of physics simulators for robotic applications. IEEE Access 9, 51416\u201351431 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3068769","journal-title":"IEEE Access"},{"key":"6_CR6","unstructured":"Dasari, S., et al..: Robonet: Large-scale multi-robot learning. CoRR abs\/1910.11215 (2019). http:\/\/arxiv.org\/abs\/1910.11215"},{"key":"6_CR7","doi-asserted-by":"publisher","unstructured":"Denninger, M., et al.: Blenderproc2: a procedural pipeline for photorealistic rendering. J. Open Source Softw. 8(82), 4901 (2023). https:\/\/doi.org\/10.21105\/joss.04901","DOI":"10.21105\/joss.04901"},{"key":"6_CR8","unstructured":"Duret, G., et al.: PickSim: a dynamically configurable Gazebo pipeline for robotic manipulation. In: Advancing Robot Manipulation Through Open-Source Ecosystems - 2023 IEEE International Conference on Robotics and Automation (ICRA) Conference Workshop (May 2023). https:\/\/hal.science\/hal-04074800"},{"key":"6_CR9","doi-asserted-by":"publisher","unstructured":"Gilles, M., Chen, Y., Robin Winter, T., Zhixuan Zeng, E., Wong, A.: MetaGraspNet: a large-scale benchmark dataset for scene-aware ambidextrous bin picking via physics-based metaverse synthesis. In: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), vol. 2022-Augus, pp. 220\u2013227. IEEE (Aug 2022). https:\/\/doi.org\/10.1109\/CASE49997.2022.9926427, https:\/\/ieeexplore.ieee.org\/document\/9926427\/","DOI":"10.1109\/CASE49997.2022.9926427"},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Gouda, A., Ghanem, A., Reining, C.: Dopose-6d dataset for object segmentation and 6d pose estimation. In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 477\u2013483 (2022). https:\/\/doi.org\/10.1109\/ICMLA55696.2022.00077","DOI":"10.1109\/ICMLA55696.2022.00077"},{"issue":"5","key":"6_CR11","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1007\/s11263-020-01323-0","volume":"128","author":"M Grard","year":"2020","unstructured":"Grard, M., Dellandr\u00e9a, E., Chen, L.: Deep multicameral decoding for localizing unoccluded object instances from a Single RGB Image. Int. J. Comput. Vision 128(5), 1331\u20131359 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01323-0","journal-title":"Int. J. Comput. Vision"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Greff, K., et al.: Kubric: a scalable dataset generator. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, pp. 3739\u20133751. IEEE (Jun 2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00373, https:\/\/github.com\/, https:\/\/ieeexplore.ieee.org\/document\/9880070\/","DOI":"10.1109\/CVPR52688.2022.00373"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In: Computer Vision\u2013ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I 11, pp. 548\u2013562. Springer (2013)","DOI":"10.1007\/978-3-642-37331-2_42"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Hodan, T., Haluza, P., Obdrzalek, S., Matas, J., Lourakis, M., Zabulis, X.: T-LESS: An RGB-D Dataset for 6D pose estimation of texture-less objects. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 880\u2013888. IEEE (mar 2017). https:\/\/doi.org\/10.1109\/WACV.2017.103, http:\/\/ieeexplore.ieee.org\/document\/7926686\/","DOI":"10.1109\/WACV.2017.103"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Hodan, T., et al.: Bop challenge 2023 on detection segmentation and pose estimation of seen and unseen rigid objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5610\u20135619 (2024)","DOI":"10.1109\/CVPRW63382.2024.00570"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., et al.: Photorealistic image synthesis for object instance detection. In: IEEE International Conference on Image Processing (ICIP) (2019)","DOI":"10.1109\/ICIP.2019.8803821"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Kaskman, R., Zakharov, S., Shugurov, I., Ilic, S.: HomebrewedDB\u202f: RGB-D Dataset for 6D Pose Estimation of 3D Objects Technical University of Munich, Germany Siemens Corporate Technology. ICCV Workshop, Germany (2019)","DOI":"10.1109\/ICCVW.2019.00338"},{"issue":"4","key":"6_CR18","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s43154-020-00021-6","volume":"1","author":"K Kleeberger","year":"2020","unstructured":"Kleeberger, K., Bormann, R., Kraus, W., Huber, M.F.: A survey on learning-based robotic grasping. Curr. Robot. Reports 1(4), 239\u2013249 (2020). https:\/\/doi.org\/10.1007\/s43154-020-00021-6","journal-title":"Curr. Robot. Reports"},{"key":"6_CR19","doi-asserted-by":"publisher","unstructured":"Kleeberger, K., Landgraf, C., Huber, M.F.: Large-scale 6D object pose estimation dataset for industrial bin-picking. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2573\u20132578. IEEE (nov 2019). https:\/\/doi.org\/10.1109\/IROS40897.2019.8967594, https:\/\/ieeexplore.ieee.org\/document\/8967594\/","DOI":"10.1109\/IROS40897.2019.8967594"},{"key":"6_CR20","doi-asserted-by":"publisher","unstructured":"Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. 2004 IEEE\/RSJ Int. Conf. Intell. Robots Syst. (IROS) 3, 2149\u20132154 (2004). https:\/\/doi.org\/10.1109\/iros.2004.1389727","DOI":"10.1109\/iros.2004.1389727"},{"key":"6_CR21","unstructured":"Lin, Y.C., Zeng, A., Song, S., Isola, P., Lin, T.Y.: Learning to see before learning to act: Visual pre-training for manipulation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 7286\u20137293 (2020). https:\/\/api.semanticscholar.org\/CorpusID:214129334"},{"key":"6_CR22","doi-asserted-by":"publisher","unstructured":"Liu, X., Iwase, S., Kitani, K.M.: StereOBJ-1M: Large-scale stereo image dataset for 6D object pose estimation. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10850\u201310859. IEEE (oct 2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01069, https:\/\/ieeexplore.ieee.org\/document\/9711414\/","DOI":"10.1109\/ICCV48922.2021.01069"},{"key":"6_CR23","unstructured":"Liu, X., et al.: GDRNPP. https:\/\/github.com\/shanice-l\/gdrnpp_bop2022 (2022)"},{"key":"6_CR24","unstructured":"Majumdar, A., et al.: Where are we in the search for an artificial visual cortex for embodied intelligence? In: Workshop on Reincarnating Reinforcement Learning at ICLR 2023 (2023). https:\/\/openreview.net\/forum?id=NJtSbIWmt2T"},{"issue":"16","key":"6_CR25","doi-asserted-by":"publisher","first-page":"24605","DOI":"10.1007\/s11042-022-14213-z","volume":"82","author":"G Marullo","year":"2023","unstructured":"Marullo, G., Tanzi, L., Piazzolla, P., Vezzetti, E.: 6d object position estimation from 2d images: a literature review. Multimed. Tools Appl. 82(16), 24605\u201324643 (2023)","journal-title":"Multimed. Tools Appl."},{"key":"6_CR26","unstructured":"Maximilian, G., Chen, Y., Winter, T.R., Zeng, E.Z., Wong, A.: MetaGraspNet: a large-scale benchmark dataset for scene-aware ambidextrous bin picking via physics-based metaverse synthesis. In: IEEE International Conference on Automation Science and Engineering (CASE) (2022)"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Mishra, S., et al.: Task2sim: towards effective pre-training and transfer from synthetic data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9194\u20139204 (June 2022)","DOI":"10.1109\/CVPR52688.2022.00898"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Muratore, F., Ramos, F., Turk, G., Yu, W., Gienger, M., Peters, J.: Robot learning from randomized simulations: a review. Front. Robot. AI 9 (2021)","DOI":"10.3389\/frobt.2022.799893"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: Pvnet: pixel-wise voting network for 6dof pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4561\u20134570 (2019)","DOI":"10.1109\/CVPR.2019.00469"},{"key":"6_CR31","doi-asserted-by":"publisher","unstructured":"Periyasamy, A.S., Schwarz, M., Behnke, S.: SynPick: a dataset for dynamic bin picking scene understanding. In: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), vol. 2021-Augus, pp. 488\u2013493. IEEE (aug 2021). https:\/\/doi.org\/10.1109\/CASE49439.2021.9551599, https:\/\/ieeexplore.ieee.org\/document\/9551599\/","DOI":"10.1109\/CASE49439.2021.9551599"},{"key":"6_CR32","unstructured":"Quigley, M., et\u00a0al.: Ros: an open-source robot operating system. In: ICRA Workshop On Open Source Software, vol.\u00a03, p.\u00a05. Kobe, Japan (2009)"},{"key":"6_CR33","doi-asserted-by":"publisher","unstructured":"Sahin, C., Garcia-Hernando, G., Sock, J., Kim, T.K.: A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators. Image Vis. Comput. 96, 103898 (2020). https:\/\/doi.org\/10.1016\/j.imavis.2020.103898, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0262885620300305","DOI":"10.1016\/j.imavis.2020.103898"},{"key":"6_CR34","doi-asserted-by":"publisher","unstructured":"Sahin, C., Kim, T.-K.: Recovering 6D object pose: a review and multi-modal analysis. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11134, pp. 15\u201331. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11024-6_2","DOI":"10.1007\/978-3-030-11024-6_2"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., et al.: Bop challenge 2022 on detection, segmentation and pose estimation of specific rigid objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2784\u20132793 (2023)","DOI":"10.1109\/CVPRW59228.2023.00279"},{"key":"6_CR36","doi-asserted-by":"publisher","unstructured":"Tremblay, J., To, T., Birchfield, S.: Falling things: a synthetic dataset for 3d object detection and pose estimation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), vol. 2018-June, pp. 2119\u201321193. IEEE (Jun 2018). https:\/\/doi.org\/10.1109\/CVPRW.2018.00275, https:\/\/ieeexplore.ieee.org\/document\/8575443\/","DOI":"10.1109\/CVPRW.2018.00275"},{"key":"6_CR37","doi-asserted-by":"publisher","unstructured":"Tyree, S., et al.: 6-DoF pose estimation of household objects for robotic manipulation: an accessible dataset and benchmark. IEEE Int. Conf. Intell. Robots Syst. 2022-Octob, 13081\u201313088 (2022). https:\/\/doi.org\/10.1109\/IROS47612.2022.9981838","DOI":"10.1109\/IROS47612.2022.9981838"},{"key":"6_CR38","unstructured":"University, R.: Rutgers APC RGB-D dataset (2016). https:\/\/robotics.cs.rutgers.edu\/pracsys\/rutgers-apc-rgb-d-dataset\/"},{"key":"6_CR39","doi-asserted-by":"publisher","unstructured":"Wang, C., Xu, D., Zhu, Y., Mart\u00edn-Mart\u00edn, R., Lu, C., Fei-Fei, L., Savarese, S.: Densefusion: 6d object pose estimation by iterative dense fusion. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3338\u20133347 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00346","DOI":"10.1109\/CVPR.2019.00346"},{"key":"6_CR40","doi-asserted-by":"crossref","unstructured":"Wang, G., Manhardt, F., Tombari, F., Ji, X.: GDR-Net: geometry-guided direct regression network for monocular 6d object pose estimation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16611\u201316621 (June 2021)","DOI":"10.1109\/CVPR46437.2021.01634"},{"key":"6_CR41","doi-asserted-by":"crossref","unstructured":"Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: Normalized object coordinate space for category-level 6d object pose and size estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)","DOI":"10.1109\/CVPR.2019.00275"},{"key":"6_CR42","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2021.789107","volume":"8","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Hirai, S., Kawamura, S.: Challenges and opportunities in robotic food handling: a review. Front. Robot. AI 8, 789107 (2022)","journal-title":"Front. Robot. AI"},{"key":"6_CR43","doi-asserted-by":"publisher","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6d object pose estimation in cluttered scenes. In: Robotics: Science and Systems XIV. Robotics: Science and Systems Foundation (Jun 2018). https:\/\/doi.org\/10.15607\/RSS.2018.XIV.019, http:\/\/www.roboticsproceedings.org\/rss14\/p19.pdf","DOI":"10.15607\/RSS.2018.XIV.019"},{"issue":"4","key":"6_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21041299","volume":"21","author":"H Yuan","year":"2021","unstructured":"Yuan, H., Hoogenkamp, T., Veltkamp, R.C.: RobotP: a benchmark dataset for 6D object pose estimation. Sensors (Switzerland) 21(4), 1\u201326 (2021). https:\/\/doi.org\/10.3390\/s21041299","journal-title":"Sensors (Switzerland)"},{"key":"6_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, M., Yao, W., Chen, C.: A review of 6d object pose estimation. In: 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol.\u00a010, pp. 1647\u20131655. IEEE (2022)","DOI":"10.1109\/ITAIC54216.2022.9836663"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91569-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:49:46Z","timestamp":1748090986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91569-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031915680","9783031915697"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91569-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}