{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T10:48:28Z","timestamp":1782470908185,"version":"3.54.5"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032009852","type":"print"},{"value":"9783032009869","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"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\/978-3-032-00986-9_4","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T23:27:35Z","timestamp":1759274855000},"page":"44-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cut-and-Splat: Leveraging Gaussian Splatting for\u00a0Synthetic Data Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8755-0002","authenticated-orcid":false,"given":"Bram","family":"Vanhele","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7071-8534","authenticated-orcid":false,"given":"Brent","family":"Zoomers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1122-6366","authenticated-orcid":false,"given":"Jeroen","family":"Put","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3705-7807","authenticated-orcid":false,"given":"Frank Van","family":"Reeth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7047-5867","authenticated-orcid":false,"given":"Nick","family":"Michiels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","unstructured":"Ara\u00fajo, A.M., Oliveira, M.M.: A robust statistics approach for plane detection in unorganized point clouds. Pattern Recogn. 100, 107115 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2019.107115","DOI":"10.1016\/j.patcog.2019.107115"},{"key":"4_CR2","unstructured":"Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., Fleet, D.J.: Synthetic data from diffusion models improves imagenet classification. Trans. Mach. Learn. Res. (2023), https:\/\/openreview.net\/forum?id=DlRsoxjyPm"},{"key":"4_CR3","unstructured":"Borkman, S., et al.: Unity perception: generate synthetic data for computer vision (2021). https:\/\/arxiv.org\/abs\/2107.04259"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009).https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206532","author":"SK Divvala","year":"2009","unstructured":"Divvala, S.K., Hoiem, D., Hays, J.H., Efros, A.A., Hebert, M.: Empirical Study Context Object Detect. (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206532","journal-title":"Empirical Study Context Object Detect."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Dvornik, N., Mairal, J., Schmid, C.: Modeling visual context is key to augmenting object detection datasets. In: Proceedings of the European Conference on Computer Vision, pp. 364\u2013380 (2018)","DOI":"10.1007\/978-3-030-01258-8_23"},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: surprisingly easy synthesis for instance detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1310\u20131319. IEEE Computer Society, Los Alamitos, CA, USA (2017).https:\/\/doi.org\/10.1109\/ICCV.2017.146","DOI":"10.1109\/ICCV.2017.146"},{"key":"4_CR8","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226\u2013231. KDD\u201996, AAAI Press (1996)"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Sun, J., Wang, R., Gou, M., Li, Y.L., Lu, C.: Instaboost: boosting instance segmentation via probability map guided copy-pasting. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 682\u2013691 (2019)","DOI":"10.1109\/ICCV.2019.00077"},{"key":"4_CR10","doi-asserted-by":"publisher","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6),pp. 381\u2013395 (1981).https:\/\/doi.org\/10.1145\/358669.358692, https:\/\/doi.org\/10.1145\/358669.358692","DOI":"10.1145\/358669.358692"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"F.R.S., K.P.: Liii. on lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2(11),pp. 559\u2013572 (1901).https:\/\/doi.org\/10.1080\/14786440109462720","DOI":"10.1080\/14786440109462720"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Ge, Y., et al.: Neural-sim: learning to generate training data with nerf (2022)","DOI":"10.1007\/978-3-031-20050-2_28"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., et al.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918\u20132928 (2021)","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"4_CR14","unstructured":"Greff, K., et al.: Kubric: a scalable dataset generator. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)"},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017).https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015). https:\/\/api.semanticscholar.org\/CorpusID:206594692","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Hinterstoisser, S., Pauly, O., Heibel, H., Martina, M., Bokeloh, M.: An annotation saved is an annotation earned: using fully synthetic training for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 2787\u20132796 (2019).https:\/\/doi.org\/10.1109\/ICCVW.2019.00340, https:\/\/doi.ieeecomputersociety.org\/10.1109\/ICCVW.2019.00340","DOI":"10.1109\/ICCVW.2019.00340"},{"key":"4_CR18","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 6840\u20136851. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4) (2023). https:\/\/repo-sam.inria.fr\/fungraph\/3d-gaussian-splatting\/","DOI":"10.1145\/3592433"},{"key":"4_CR20","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Li, D., Ling, H., Kim, S.W., Kreis, K., Fidler, S., Torralba, A.: Bigdatasetgan: synthesizing imagenet with pixel-wise annotations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21298\u201321308 (2022)","DOI":"10.1109\/CVPR52688.2022.02064"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common Objects in Context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Meyer, L., Erich, F., Yoshiyasu, Y., Stamminger, M., Ando, N., Domae, Y.: Pegasus: physical enhanced gaussian splatting simulation system for 6dof object pose dataset generation. In: Proceedings of the International Conference on Intelligent Robots and Systems (2024). https:\/\/meyerls.github.io\/pegasus_web","DOI":"10.1109\/IROS58592.2024.10802037"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Moonen, S., Vanherle, B., de\u00a0Hoog, J., Bourgana, T., Bey-Temsamani, A., Michiels, N.: Cad2render: A modular toolkit for GPU-accelerated photorealistic synthetic data generation for the manufacturing industry. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops, pp. 583\u2013592 (2023)","DOI":"10.1109\/WACVW58289.2023.00065"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Oza, P., Sindagi, V.A., Sharmini, V.V., Patel, V.M.: Unsupervised domain adaptation of object detectors: a survey. In: IEEE Transactions on Pattern Analysis and Machine Intelligence,pp. 1\u201324 (2023).https:\/\/doi.org\/10.1109\/TPAMI.2022.3217046","DOI":"10.1109\/TPAMI.2022.3217046"},{"key":"4_CR26","unstructured":"Podell, D., et al.: SDXL: improving latent diffusion models for high-resolution image synthesis. In: Proceedings of the International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=di52zR8xgf"},{"key":"4_CR27","doi-asserted-by":"publisher","unstructured":"Sch\u00f6nberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4104\u20134113 (2016).https:\/\/doi.org\/10.1109\/CVPR.2016.445","DOI":"10.1109\/CVPR.2016.445"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Tremblay, J., et al.: Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00143"},{"key":"4_CR29","doi-asserted-by":"publisher","unstructured":"Tripathi, S., Chandra, S., Agrawal, A., Tyagi, A., Rehg, J.M., Chari, V.: Learning to generate synthetic data via compositing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 461\u2013470. IEEE Computer Society, Los Alamitos, CA, USA (2019).https:\/\/doi.org\/10.1109\/CVPR.2019.00055, https:\/\/doi.ieeecomputersociety.org\/10.1109\/CVPR.2019.00055","DOI":"10.1109\/CVPR.2019.00055"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Wood, E., Baltru\u0161aitis, T., Hewitt, C., Dziadzio, S., Cashman, T.J., Shotton, J.: Fake it till you make it: face analysis in the wild using synthetic data alone. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3681\u20133691 (2021)","DOI":"10.1109\/ICCV48922.2021.00366"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhao, Y., Shou, M.Z., Zhou, H., Shen, C.: Diffumask: synthesizing images with pixel-level annotations for semantic segmentation using diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1206\u20131217 (2023). https:\/\/api.semanticscholar.org\/CorpusID:257636752","DOI":"10.1109\/ICCV51070.2023.00117"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: unleashing the power of large-scale unlabeled data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2024)","DOI":"10.1109\/CVPR52733.2024.00987"},{"key":"4_CR33","unstructured":"Zhou, Q.Y., Park, J., Koltun, V.: Open3D: a modern library for 3D data processing. arXiv:1801.09847 (2018)"}],"container-title":["Communications in Computer and Information Science","Robotics, Computer Vision and Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00986-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T09:59:55Z","timestamp":1782467995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00986-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032009852","9783032009869"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00986-9_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ROBOVIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Robotics, Computer Vision and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 February 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 February 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"robovis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/robovis.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}