{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:22:20Z","timestamp":1774858940812,"version":"3.50.1"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Robot Syst"],"DOI":"10.1007\/s10846-025-02340-2","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T01:36:13Z","timestamp":1766540173000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A High-fidelity Multimodal Synthetic Dataset Generation Framework for Off-road Unstructured Terrain Navigation Training of Autonomous Robots"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9058-1790","authenticated-orcid":false,"given":"Liyana","family":"Wijayathunga","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dulitha","family":"Dabare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Rassau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Mohammed Shamsul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"issue":"3","key":"2340_CR1","doi-asserted-by":"publisher","first-page":"8138","DOI":"10.1109\/LRA.2022.3187278","volume":"7","author":"T Guan","year":"2022","unstructured":"Guan, T., Kothandaraman, D., Chandra, R., Sathyamoorthy, A.J., Weerakoon, K., Manocha, D.: Ga-nav: efficient terrain segmentation for robot navigation in unstructured outdoor environments. IEEE Robot. Autom. Lett. 7(3), 8138\u20138145 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"2340_CR2","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/978-3-319-67361-5_22","volume-title":"Field and Service Robotics: Results of the 11th International Conference","author":"D Maturana","year":"2018","unstructured":"Maturana, D., Chou, P.W., Uenoyama, M., Scherer, S.: Real-time semantic mapping for autonomous off-road navigation. In: Field and Service Robotics: Results of the 11th International Conference, pp. 335\u2013350. Springer (2018)"},{"issue":"11\u201312","key":"2340_CR3","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1002\/rob.20161","volume":"23","author":"LD Jackel","year":"2006","unstructured":"Jackel, L.D., Krotkov, E., Perschbacher, M., Pippine, J., Sullivan, C.: The DARPA LAGR program: goals, challenges, methodology, and phase I results. Journal of Field Robotics 23(11\u201312), 945\u2013973 (2006)","journal-title":"Journal of Field Robotics"},{"key":"2340_CR4","first-page":"4","volume-title":"European Conference on Machine Learning","author":"S Thrun","year":"2006","unstructured":"Thrun, S.: Winning the DARPA grand challenge. In: European Conference on Machine Learning, pp. 4\u20134. Springer (2006)"},{"key":"2340_CR5","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The Cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213\u20133223. (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"2340_CR6","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., Darrell, T.: \u201cBDD100K: A diverse driving dataset for heterogeneous multitask learning,\u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,pp. 2636\u20132645. (2020","DOI":"10.1109\/CVPR42600.2020.00271"},{"issue":"11\u201312","key":"2340_CR7","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11\u201312), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"key":"2340_CR8","doi-asserted-by":"crossref","unstructured":"Wooden, D., Malchano, M., Blankespoor, K., Howardy, A., Rizzi, A.A., Raibert, M.: Autonomous navigation for BigDog. In: 2010 IEEE international conference on robotics and automation. IEEE, pp. 4736\u20134741. (2010)","DOI":"10.1109\/ROBOT.2010.5509226"},{"key":"2340_CR9","doi-asserted-by":"crossref","unstructured":"Kolski, S., Ferguson, D., Bellino, M., Siegwart, R.: Autonomous driving in structured and unstructured environments, in 2006 IEEE Intelligent Vehicles Symposium. IEEE, pp. 558\u2013563. (2006)","DOI":"10.1109\/IVS.2006.1689687"},{"key":"2340_CR10","doi-asserted-by":"crossref","unstructured":"Ort, T., Paull, L., Rus, D.: Autonomous vehicle navigation in rural environments without detailed prior maps, In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 2040\u20132047. (2018)","DOI":"10.1109\/ICRA.2018.8460519"},{"key":"2340_CR11","doi-asserted-by":"crossref","unstructured":"Varma, G., Subramanian, A., Namboodiri, A., Chandraker, M., Jawahar, C.: IDD: A dataset for exploring problems of autonomous navigation in unconstrained environments, in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 1743\u20131751. (2019)","DOI":"10.1109\/WACV.2019.00190"},{"key":"2340_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, P., Osteen, P., Wigness, M., Saripalli, S.: RELLIS-3D dataset: Data, benchmarks and analysis, In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1110\u20131116. (2021)","DOI":"10.1109\/ICRA48506.2021.9561251"},{"key":"2340_CR13","doi-asserted-by":"crossref","unstructured":"Wijayathunga, L., Dabare, D., Rassau, A., Chai, D., Islam, S.M.S.: OUTBACK: A multimodal synthetic dataset for rural Australian off-road robot navigation, In: 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, pp. 397\u2013402. (2024)","DOI":"10.1109\/DICTA63115.2024.00065"},{"key":"2340_CR14","doi-asserted-by":"crossref","unstructured":"Paranjape, I., Jawad, A., Xu, Y., Song, A., Whitehead, J.: A modular architecture for procedural generation of towns, intersections and scenarios for testing autonomous vehicles, In: 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, pp. 162\u2013168. (2020)","DOI":"10.1109\/IV47402.2020.9304625"},{"issue":"17","key":"2340_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/app12178429","volume":"12","author":"M Rojas","year":"2022","unstructured":"Rojas, M., Hermosilla, G., Yunge, D., Farias, G.: An easy to use deep reinforcement learning library for AI mobile robots in Isaac Sim. Applied Sciences 12(17), 8429 (2022)","journal-title":"Applied Sciences"},{"issue":"2","key":"2340_CR16","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: a high-definition ground truth database. Pattern Recogn. Lett. 30(2), 88\u201397 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"2340_CR17","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 633\u2013641. (2017)","DOI":"10.1109\/CVPR.2017.544"},{"key":"2340_CR18","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13","author":"TY Lin","year":"2014","unstructured":"Lin, T.Y., et al.: Microsoft COCO: Common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13, pp. 740\u2013755. Springer (2014)"},{"key":"2340_CR19","doi-asserted-by":"crossref","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, IEEE, pp. 248\u2013255. (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"89","key":"2340_CR20","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.adi9641","volume":"9","author":"J Lee","year":"2024","unstructured":"Lee, J., Bjelonic, M., Reske, A., Wellhausen, L., Miki, T., Hutter, M.: Learning robust autonomous navigation and locomotion for wheeled-legged robots. Science Robotics 9(89), eadi9641 (2024)","journal-title":"Science Robotics"},{"key":"2340_CR21","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10765\u201310775. (2021)","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"2340_CR22","unstructured":"Kenk, M.A., Hassaballah, M.: DAWN: Vehicle detection in adverse weather nature dataset. arXiv preprint.https:\/\/arxiv.org\/abs\/2008.05402. (2020)"},{"issue":"20","key":"2340_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/s23208471","volume":"23","author":"D Kumar","year":"2023","unstructured":"Kumar, D., Muhammad, N.: Object detection in adverse weather for autonomous driving through data merging and YOLOv8. Sensors 23(20), 8471 (2023). ([Online]. Available:)","journal-title":"Sensors"},{"issue":"1","key":"2340_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-68115-1","volume":"14","author":"Y Li","year":"2024","unstructured":"Li, Y., Huang, Y., Tao, Q.: Improving real-time object detection in Internet-of-Things smart city traffic with YOLOv8-DSAF method. Sci. Rep. 14(1), 17235 (2024). https:\/\/doi.org\/10.1038\/s41598-024-68115-1","journal-title":"Sci. Rep."},{"key":"2340_CR25","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhao, Z., Zhao, H.: UniMatch v2: Pushing the limit of semi-supervised semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence. (2025)","DOI":"10.1109\/TPAMI.2025.3528453"},{"key":"2340_CR26","doi-asserted-by":"crossref","unstructured":"Jiao, J., Geng, R., Li, Y., Xin, R, Yang, B., Wu, J., Wang, L., Liu, M., Fan, R., Kanoulas, D.: Real-time metric-semantic mapping for autonomous navigation in outdoor environments, IEEE Transactions on Automation Science and Engineering. (2024)","DOI":"10.1109\/TASE.2024.3429280"},{"key":"2340_CR27","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3234\u20133243. (2016)","DOI":"10.1109\/CVPR.2016.352"},{"key":"2340_CR28","doi-asserted-by":"crossref","unstructured":"Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4340\u20134349. (2016)","DOI":"10.1109\/CVPR.2016.470"},{"key":"2340_CR29","doi-asserted-by":"publisher","first-page":"161","DOI":"10.5194\/isprs-annals-IV-4-W4-161-2017","volume":"4","author":"I Buyuksalih","year":"2017","unstructured":"Buyuksalih, I., Bayburt, S., Buyuksalih, G., Baskaraca, A., Karim, H., Rahman, A.A.: 3D modelling and visualization based on the Unity game engine\u2013advantages and challenges. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 4, 161\u2013166 (2017)","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"2340_CR30","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.,; CARLA: An open urban driving simulator. In: Conference on robot Learning. PMLR, pp. 1\u201316. (2017)"},{"key":"2340_CR31","unstructured":"Khan, S., Phan, B., Salay, R., Czarnecki, K.: ProcSy: Procedural synthetic dataset generation towards influence factors studies of semantic segmentation networks. CVPR Workshops 3, p. 4.(2019)."},{"key":"2340_CR32","doi-asserted-by":"crossref","unstructured":"Dugas, D., Andersson, O., Siegwart, R., Chung, J.J.: NavDreams: Towards camera-only RL navigation among humans. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2504\u20132511. (2022)","DOI":"10.1109\/IROS47612.2022.9982045"},{"key":"2340_CR33","doi-asserted-by":"crossref","unstructured":"Wigness, M., Eum, S., Rogers, J.G., Han, D., Kwon, H.: A RUGD dataset for autonomous navigation and visual perception in unstructured outdoor environments. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 5000\u20135007.(2019","DOI":"10.1109\/IROS40897.2019.8968283"},{"key":"2340_CR34","doi-asserted-by":"crossref","unstructured":"Mortimer, P., Hagmanns, R., Granero, M., Luettel, T., Petereit, J., Wuensche, H.J.: The GOOSE dataset for perception in unstructured environments. In: 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 14 838\u201314 844. (2024)","DOI":"10.1109\/ICRA57147.2024.10611298"},{"key":"2340_CR35","doi-asserted-by":"crossref","unstructured":"Sharma, L., Everett, M., Lee, D., Cai, X., Osteen, P.R., How, J.P.: RAMP: A risk-aware mapping and planning pipeline for fast off-road ground robot navigation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 5730\u2013573. (2023)","DOI":"10.1109\/ICRA48891.2023.10160602"},{"key":"2340_CR36","doi-asserted-by":"publisher","first-page":"24759","DOI":"10.1109\/ACCESS.2022.3154419","volume":"10","author":"S Sharma","year":"2022","unstructured":"Sharma, S., et al.: Cat: cavs traversability dataset for off-road autonomous driving. IEEE Access 10, 24759\u201324768 (2022)","journal-title":"IEEE Access"},{"key":"2340_CR37","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/978-3-319-50115-4_41","volume-title":"2016 International Symposium on Experimental Robotics","author":"A Valada","year":"2017","unstructured":"Valada, A., Oliveira, G.L., Brox, T., Burgard, W.: Deep multispectral semantic scene understanding of forested environments using multimodal fusion. In: 2016 International Symposium on Experimental Robotics, pp. 465\u2013477. Springer (2017)"},{"key":"2340_CR38","doi-asserted-by":"crossref","unstructured":"Sivaprakasam, M., Maheshwari, P., Castro, M.G., Triest, S., Nye, M., Willits, S., Saba, A., W. Wang, W., Scherer, S.: Tartandrive 2.0: More modalities and better infrastructure to further self-supervised learning research in off-road driving tasks. In: 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 12 606\u201312 606. (2024)","DOI":"10.1109\/ICRA57147.2024.10611265"},{"key":"2340_CR39","doi-asserted-by":"publisher","DOI":"10.1177\/02783649241278369","author":"K Vidanapathirana","year":"2024","unstructured":"Vidanapathirana, K., et al.: Wildscenes: a benchmark for 2D and 3D semantic segmentation in large-scale natural environments. The International Journal of Robotics Research (2024). https:\/\/doi.org\/10.1177\/02783649241278369","journal-title":"The International Journal of Robotics Research"},{"key":"2340_CR40","unstructured":"Bittner, D., Andrada, M.E., Portugal, D., Ferreira, J.F.: SEMFIRE forest dataset for semantic segmentation and data augmentation. [Online]. Available: https:\/\/zenodo.org\/records\/5819064. Accessed 10 Apr 2025. (2021)"},{"issue":"2","key":"2340_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-024-02114-2","volume":"110","author":"K Ma\u0142ek","year":"2024","unstructured":"Ma\u0142ek, K., Dyba\u0142a, J., Kordecki, A., Hondra, P., Kijania, K.: OffRoadSynth open dataset for semantic segmentation using synthetic-data-based weight initialization for autonomous UGV in off-road environments. J. Intell. Robot. Syst. 110(2), 76 (2024)","journal-title":"J. Intell. Robot. Syst."},{"issue":"15","key":"2340_CR42","doi-asserted-by":"publisher","first-page":"5599","DOI":"10.3390\/s22155599","volume":"22","author":"M S\u00e1nchez","year":"2022","unstructured":"S\u00e1nchez, M., Morales, J., Mart\u00ednez, J.L., Fern\u00e1ndez-Lozano, J.J., Garc\u00eda-Cerezo, A.: Automatically annotated dataset of a ground mobile robot in natural environments via gazebo simulations. Sensors 22(15), 5599 (2022)","journal-title":"Sensors"},{"issue":"5","key":"2340_CR43","first-page":"4979","volume":"53","author":"D Chen","year":"2023","unstructured":"Chen, D., Zhuang, M., Zhong, X., Wu, W., Liu, Q.: RSPMP: Real-time semantic perception and motion planning for autonomous navigation of unmanned ground vehicle in off-road environments. Appl. Intell. 53(5), 4979\u20134995 (2023)","journal-title":"Appl. Intell."},{"key":"2340_CR44","doi-asserted-by":"crossref","unstructured":"Selee, B., Faykus, M., Smith, M.:Semantic segmentation with high inference speed in off-road environments. SAE Technical Paper, Tech. Rep. pp. 0148\u20137191. (2023)","DOI":"10.4271\/2023-01-0868"},{"key":"2340_CR45","doi-asserted-by":"crossref","unstructured":"Cai, X., Everett, M., Fink, J., How, J.P.:Risk-aware off-road navigation via a learned speed distribution map. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2931\u20132937. (2022)","DOI":"10.1109\/IROS47612.2022.9982200"},{"issue":"3","key":"2340_CR46","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1109\/LRA.2018.2801794","volume":"3","author":"RO Chavez-Garcia","year":"2018","unstructured":"Chavez-Garcia, R.O., Guzzi, J., Gambardella, L.M., Giusti, A.: Learning ground traversability from simulations. IEEE Robot. Autom. Lett. 3(3), 1695\u20131702 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"2340_CR47","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-3-319-99229-7_37","volume-title":"Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, V\u00e4ster\u00e5s, Sweden, September 18, 2018, Proceedings 37","author":"K Czarnecki","year":"2018","unstructured":"Czarnecki, K., Salay, R.: Towards a framework to manage perceptual uncertainty for safe automated driving. In: Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, V\u00e4ster\u00e5s, Sweden, September 18, 2018, Proceedings 37, pp. 439\u2013445. Springer (2018)"},{"key":"2340_CR48","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, M., Mart\u00ednez, J.L., Morales, J., Robles, A., Mor\u00e1n, M.: Automatic generation of labelled 3D point clouds of natural environments with Gazebo. In: 2019 IEEE International Conference on Mechatronics (ICM), vol. 1. IEEE, pp. 161\u2013166. (2019)","DOI":"10.1109\/ICMECH.2019.8722866"},{"key":"2340_CR49","unstructured":"Hamilton, A.S.: Rural Australia. [Online]. Available: https:\/\/www.fab.com\/listings\/1c1467ce-a2f5-4be1-8988-9069f90a8571. Accessed 4 Apr 2025"},{"key":"2340_CR50","doi-asserted-by":"crossref","unstructured":"Over, J.S.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D.D., Noble, T., Sherwood, C.R, Warrick, J.A., Wernette, P.A. \u201cProcessing coastal imagery with Agisoft Metashape professional edition, version 1.6: Structure-from-motion workflow documentation,\u201d US Geological Survey, Tech. Rep., pp 2331\u20131258. (2021)","DOI":"10.3133\/ofr20211039"},{"key":"2340_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.106321","volume":"33","author":"R Hellmuth","year":"2020","unstructured":"Hellmuth, R., Wehner, F., Giannakidis, A.: Datasets of captured images of three different devices for photogrammetry calculation comparison and integration into a laserscan point cloud of a built environment. Data Brief 33, 106321 (2020)","journal-title":"Data Brief"},{"key":"2340_CR52","doi-asserted-by":"crossref","unstructured":"Griwodz, C., Gasparini, S., Calvet, L., Gurdjos, P., Castan, F., Maujean, B., De Lillo, G., Lanthony, Y.: AliceVision Meshroom: An open-source 3D reconstruction pipeline. In: Proceedings of the 12th ACM multimedia systems conference, pp. 241\u2013247. (2021)","DOI":"10.1145\/3458305.3478443"},{"issue":"4","key":"2340_CR53","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/s40799-021-00491-z","volume":"46","author":"D Rohe","year":"2022","unstructured":"Rohe, D., Jones, E.: Generation of synthetic digital image correlation images using the open-source blender software. Exp. Tech. 46(4), 615\u2013631 (2022)","journal-title":"Exp. Tech."},{"key":"2340_CR54","unstructured":"Tickoo, S.: MAXON ZBrush 2023: A Comprehensive Guide. CADCIM Technologies. (2023)"},{"key":"2340_CR55","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Tran, D.N.N., Jeon, J.W.: Real-time semantic segmentation on edge devices with NVIDIA Jetson AGX Xavier. In: 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 1\u20134. IEEE (2022)","DOI":"10.1109\/ICCE-Asia57006.2022.9954835"},{"issue":"12","key":"2340_CR56","doi-asserted-by":"publisher","first-page":"5958","DOI":"10.3390\/app12125958","volume":"12","author":"A Bustamante","year":"2022","unstructured":"Bustamante, A., Belmonte, L.M., Morales, R., Pereira, A., Fern\u00e1ndez-Caballero, A.: Video processing from a virtual unmanned aerial vehicle: comparing two approaches to using OpenCV in Unity. Appl. Sci. 12(12), 5958 (2022)","journal-title":"Appl. Sci."},{"key":"2340_CR57","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.procs.2023.12.036","volume":"229","author":"I Reutov","year":"2023","unstructured":"Reutov, I.: Generating of synthetic datasets using diffusion models for solving computer vision tasks in urban applications. Procedia Comput. Sci. 229, 335\u2013344 (2023)","journal-title":"Procedia Comput. Sci."},{"key":"2340_CR58","doi-asserted-by":"crossref","unstructured":"Wijayathunga, L., Dabare, D., Rassau, A., Chai, D.,and Islam. S.M.S.: OUTBACK: A multimodal synthetic dataset for rural australian off-road robot navigation. [Online]. Available: https:\/\/github.com\/liyanawijaya\/outback. Accessed 14 Apr 2025.","DOI":"10.1109\/DICTA63115.2024.00065"},{"key":"2340_CR59","doi-asserted-by":"crossref","unstructured":"Jain, J., Li, J., Chiu, M.T., Hassani, A., Orlov, N., Shi, H.: OneFormer: One transformer to rule universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2989\u20132998. (2023)","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"2340_CR60","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., Sang, N.: BiSeNetV2: bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vis. 129, 3051\u20133068 (2021)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Journal of Intelligent &amp; Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10846-025-02340-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-025-02340-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-025-02340-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T07:39:36Z","timestamp":1774856376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10846-025-02340-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,24]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["2340"],"URL":"https:\/\/doi.org\/10.1007\/s10846-025-02340-2","relation":{},"ISSN":["1573-0409"],"issn-type":[{"value":"1573-0409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,24]]},"assertion":[{"value":"23 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"10"}}