{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T11:18:15Z","timestamp":1775215095463,"version":"3.50.1"},"reference-count":137,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012320","name":"Otto-von-Guericke-Universit\u00e4t Magdeburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012320","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Reliable 3D detection is fundamental to autonomous systems such as mobile robots, self-driving cars, and unmanned aerial vehicles (UAVs). To achieve this capability, researchers have developed and trained supervised networks, which require large amounts of diverse and precisely annotated data. Due to the complex, expensive, and time-consuming capturing and annotation process, synthetic dataset generation approaches have gained popularity over the last decade. With increasing computational resources and advances in simulation technologies, a variety of dataset generators have emerged. These methods rely on either traditional 3D modeling or neural image synthesis to generate data for specific scenarios or general-purpose 3D detection tasks. Their primary goal is to produce high-quality, annotated 3D datasets in an automated and scalable manner. In this review, we evaluate the extent to which state-of-the-art approaches fulfill this goal by introducing a categorization scheme and conducting a comprehensive analysis of both 3D modeling and neural synthesis methods. Our analysis includes techniques used to address the Sim-to-Real domain gap. Furthermore, we assess each method\u2019s level of automation, prerequisites, and practical adoption. This review aims to guide the reader in selecting automated dataset generation workflows for specific detection problems. By considering dataset quality, prerequisites, and application scenarios, we offer practical insights into identifying suitable methods for diverse downstream tasks.<\/jats:p>","DOI":"10.1007\/s10462-025-11431-3","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T23:48:12Z","timestamp":1763077692000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automating synthetic dataset generation for image-based 3D detection: a literature review"],"prefix":"10.1007","volume":"59","author":[{"given":"Paul","family":"Schulz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thorsten","family":"Hempel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magnus","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayoub","family":"Al-Hamadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"11431_CR1","doi-asserted-by":"crossref","unstructured":"Ahmadyan A, Zhang L, Wei J, Ablavatski A, Grundmann M (2020) Objectron: a large scale dataset of object-centric videos in the wild with pose annotations. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7818\u20137827","DOI":"10.1109\/CVPR46437.2021.00773"},{"issue":"17","key":"11431_CR2","doi-asserted-by":"publisher","first-page":"18263","DOI":"10.1007\/s11042-024-19731-6","volume":"84","author":"CA Akar","year":"2024","unstructured":"Akar CA, Tekli J, Khalil J, Yaghi A, Haddad Y, Makhoul A, Kamradt M (2024) Sordi.ai: large-scale synthetic object recognition dataset generation for industries. Multimed Tools Appl 84(17):18263\u201318304","journal-title":"Multimed Tools Appl"},{"key":"11431_CR3","doi-asserted-by":"crossref","unstructured":"Anagnostopoulou D, Retsinas G, Efthymiou N, Filntisis PP, Maragos P (2023) A realistic synthetic mushroom scenes dataset. In: 2023 IEEE\/CVF Conference on computer vision and pattern recognition workshops (CVPRW), pp 6282\u20136289","DOI":"10.1109\/CVPRW59228.2023.00668"},{"key":"11431_CR4","doi-asserted-by":"crossref","unstructured":"Bai K, Zhang L, Chen Z, Wan F, Zhang J (2024) Close the sim2real gap via physically-based structured light synthetic data simulation. In: 2024 IEEE international conference on robotics and automation (ICRA), pp 17035\u201317041","DOI":"10.1109\/ICRA57147.2024.10611401"},{"key":"11431_CR5","doi-asserted-by":"crossref","unstructured":"Barragan JA, Zhang J, Zhou H, Munawar A, Kazanzides P (2024) Realistic data generation for 6d pose estimation of surgical instruments. In: 2024 IEEE international conference on robotics and automation (ICRA), pp 13347\u201313353","DOI":"10.1109\/ICRA57147.2024.10611638"},{"key":"11431_CR6","doi-asserted-by":"crossref","unstructured":"Barra S, Marras M, Mohamed S, Podda AS, Saia R (2023) Can existing 3d monocular object detection methods work in roadside contexts? a reproducibility study. In: International conference of the italian association for artificial intelligence. https:\/\/api.semanticscholar.org\/CorpusID:265051747","DOI":"10.1007\/978-3-031-47546-7_22"},{"key":"11431_CR7","unstructured":"Blender Institute (2024) : Blender. https:\/\/www.blender.org\/"},{"key":"11431_CR8","doi-asserted-by":"crossref","unstructured":"Blomqvist K, Chung JJ, Ott L, Siegwart RY (2022) Semi-automatic 3d object keypoint annotation and detection for the masses. In: 2022 26th International conference on pattern recognition (ICPR), pp 3908\u20133914","DOI":"10.1109\/ICPR56361.2022.9956263"},{"key":"11431_CR9","doi-asserted-by":"crossref","unstructured":"Blomqvist K, Chung JJ, Ott L, Siegwart RY (2023) Nerfing it: offline object segmentation through implicit modeling. In: 2023 IEEE International conference on robotics and automation (ICRA), pp 9407\u20139413","DOI":"10.1109\/ICRA48891.2023.10161040"},{"key":"11431_CR10","unstructured":"Borkman S, Crespi A, Dhakad S, Ganguly S, Hogins J, Jhang YC, Kamalzadeh M, Li B, Leal S, Parisi P, Romero C, Smith W, Thaman A, Warren S, Yadav N (2021) Unity perception: generate synthetic data for computer vision. arXiv:abs\/2107.04259"},{"key":"11431_CR11","doi-asserted-by":"crossref","unstructured":"Brazil G, Kumar A, Straub J, Ravi N, Johnson J, Gkioxari G (2022) Omni3d: a large benchmark and model for 3d object detection in the wild. In: 2023 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 13154\u201313164","DOI":"10.1109\/CVPR52729.2023.01264"},{"key":"11431_CR12","doi-asserted-by":"crossref","unstructured":"Caesar H, Bankiti V, Lang AH, Vora S, Liong VE, Xu Q, Krishnan A, Pan Y, Baldan G, Beijbom O (2019) Nuscenes: a multimodal dataset for autonomous driving. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 11618\u201311628","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"11431_CR13","doi-asserted-by":"crossref","unstructured":"\u00c7alli B, Singh A, Walsman A, Srinivasa SS, Abbeel P, Dollar AM (2015) The ycb object and model set: towards common benchmarks for manipulation research. In: 2015 international conference on advanced robotics (ICAR), pp 510\u2013517","DOI":"10.1109\/ICAR.2015.7251504"},{"key":"11431_CR14","doi-asserted-by":"crossref","unstructured":"Carta S, Castrill\u00f3n-Santana M, Marras M, Mohamed S, Podda AS, Saia R, Sau M, Zimmer W (2024) Roadsense3d: a framework for roadside monocular 3d object detection. In: Adjunct Proceedings of the 32nd ACM conference on user modeling, adaptation and personalization","DOI":"10.1145\/3631700.3665236"},{"key":"11431_CR15","doi-asserted-by":"crossref","unstructured":"Chen T, Ren T, Niu J, Li Q (2021) A novel shape-based robotic sorting approach based on computer vision. In: 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom), pp 660\u2013667","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00096"},{"key":"11431_CR16","unstructured":"Chen K, Xie E, Chen Z, Hong L, Li Z, Yeung D-Y (2023) Geodiffusion: text-prompted geometric control for object detection data generation. In: International Conference on Learning Representations. https:\/\/api.semanticscholar.org\/CorpusID:259096176"},{"key":"11431_CR17","doi-asserted-by":"crossref","unstructured":"Costanzo M, Simone MD, Federico S, Natale C, Pirozzi S (2023) Enhanced 6d pose estimation for robotic fruit picking. In: 2023 9th International conference on control, decision and information technologies (CoDIT), pp 901\u2013906","DOI":"10.1109\/CoDIT58514.2023.10284072"},{"key":"11431_CR18","doi-asserted-by":"crossref","unstructured":"Dai A, Chang AX, Savva M, Halber M, Funkhouser TA, Nie\u00dfner M (2017) Scannet: richly-annotated 3d reconstructions of indoor scenes. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2432\u20132443","DOI":"10.1109\/CVPR.2017.261"},{"key":"11431_CR20","unstructured":"Denninger M, Sundermeyer M, Winkelbauer D, Olefir D, Hodan T, Zidan Y, Elbadrawy M, Knauer MW, Katam H, Lodhi A (2020) Blenderproc: reducing the reality gap with photorealistic rendering"},{"key":"11431_CR19","doi-asserted-by":"publisher","first-page":"4901","DOI":"10.21105\/joss.04901","volume":"8","author":"M Denninger","year":"2023","unstructured":"Denninger M, Winkelbauer D, Sundermeyer M, Boerdijk W, Knauer M, Strobl KH, Humt M, Triebel R (2023) Blenderproc2: a procedural pipeline for photorealistic rendering. J Open Source Softw 8:4901","journal-title":"J Open Source Softw"},{"key":"11431_CR21","doi-asserted-by":"crossref","unstructured":"Doruk AE, Ozkaya TE, G\u00fclmez F, Uslu F (2023) A comparative study for 6d pose estimation of textureless and symmetric objects used in automotive manufacturing industry. In: 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp 1\u20137","DOI":"10.1109\/HORA58378.2023.10156677"},{"key":"11431_CR22","unstructured":"Dosovitskiy A, Ros G, Codevilla F, L\u00f3pez AM, Koltun V (2017) Carla: an open urban driving simulator. In: Conference on Robot Learning. https:\/\/api.semanticscholar.org\/CorpusID:5550767"},{"key":"11431_CR23","doi-asserted-by":"crossref","unstructured":"Drobnitzky M, Friederich J, Egger B, Zschech P (2022) Survey and systematization of 3d object detection models and methods. arXiv:abs\/2201.09354","DOI":"10.1007\/s00371-023-02891-1"},{"key":"11431_CR24","unstructured":"Epic Games (2024) : Unreal Engine. https:\/\/www.unrealengine.com"},{"key":"11431_CR25","doi-asserted-by":"crossref","unstructured":"Filipovic B, Sikic F, Kalafatic Z, Loncaric S, Subavsic M (2023) Detection of tea box orientations in retail shelves images. In: 2023 International symposium on image and signal processing and analysis (ISPA), pp 1\u20135","DOI":"10.1109\/ISPA58351.2023.10279661"},{"key":"11431_CR26","doi-asserted-by":"crossref","unstructured":"Firman M (2016) Rgbd datasets: past, present and future. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 661\u2013673","DOI":"10.1109\/CVPRW.2016.88"},{"key":"11431_CR27","doi-asserted-by":"crossref","unstructured":"Gaidon A, Wang Q, Cabon Y, Vig E (2016) Virtualworlds as proxy for multi-object tracking analysis. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 4340\u20134349","DOI":"10.1109\/CVPR.2016.470"},{"key":"11431_CR29","unstructured":"Gao R, Chen K, Xie E, Hong L, Li Z, Yeung D-Y, Xu Q (2023) Magicdrive: street view generation with diverse 3d geometry control. arXiv:abs\/2310.02601"},{"key":"11431_CR28","unstructured":"Gao R, Chen K, Xiao B, Hong L, Li Z, Xu Q (2024) Magicdrive-v2: high-resolution long video generation for autonomous driving with adaptive control. https:\/\/api.semanticscholar.org\/CorpusID:274165928"},{"key":"11431_CR30","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia A, Martinez-Gonzalez P, Oprea S, Castro-Vargas JA, Orts S, Rodr\u00edguez JG, Jover-Alvarez A (2018) The robotrix: an extremely photorealistic and very-large-scale indoor dataset of sequences with robot trajectories and interactions. In: 2018 IEEE\/RSJ International conference on intelligent robots and systems (IROS), pp 6790\u20136797 (2018)","DOI":"10.1109\/IROS.2018.8594495"},{"key":"11431_CR31","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"11431_CR32","doi-asserted-by":"crossref","unstructured":"Ge Y, Tang Y, Xu J, Gokmen C, Li C, Ai W, Martinez BJ, Aydin A, Anvari M, Chakravarthy AK, Yu H-X, Wong J, Srivastava S, Lee S, Zha SC, Itti L, Li Y, Mart\u2019in-Mart\u2019in R, Liu M, Zhang P, Zhang R, Li F-F, Wu J (2024) Behavior vision suite: customizable dataset generation via simulation. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 22401\u201322412","DOI":"10.1109\/CVPR52733.2024.02114"},{"key":"11431_CR33","doi-asserted-by":"crossref","unstructured":"Grau O, Hagn K (2023) Valerie22 - a photorealistic, richly metadata annotated dataset of urban environments. Proceedings of the 7th ACM computer science in cars symposium","DOI":"10.1145\/3631204.3631866"},{"key":"11431_CR34","doi-asserted-by":"crossref","unstructured":"Greff K, Belletti F, Beyer L, Doersch C, Du Y, Duckworth D, Fleet DJ, Gnanapragasam D, Golemo F, Herrmann C, Kipf T, Kundu A, Lagun D, Laradji IH, Liu H-T, Meyer H, Miao Y, Nowrouzezahrai D, Oztireli C, Pot E, Radwan N, Rebain D, Sabour S, Sajjadi MSM, Sela M, Sitzmann V, Stone A, Sun D, Vora S, Wang Z, Wu T, Yi KM, Zhong F, Tagliasacchi A (2022) Kubric: a scalable dataset generator. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3739\u20133751","DOI":"10.1109\/CVPR52688.2022.00373"},{"key":"11431_CR35","doi-asserted-by":"crossref","unstructured":"Guo A, Wen B, Yuan J, Tremblay J, Tyree S, Smith J, Birchfield S (2023) Handal: a dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions. In: 2023 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 11428\u201311435","DOI":"10.1109\/IROS55552.2023.10341672"},{"key":"11431_CR36","doi-asserted-by":"publisher","first-page":"2377","DOI":"10.1109\/TIP.2024.3378180","volume":"33","author":"T Hempel","year":"2023","unstructured":"Hempel T, Abdelrahman AA, Al-Hamadi A (2023) Toward robust and unconstrained full range of rotation head pose estimation. IEEE Trans Image Process 33:2377\u20132387","journal-title":"IEEE Trans Image Process"},{"key":"11431_CR37","doi-asserted-by":"crossref","unstructured":"Hodan T, Haluza P, Obdrz\u00e1lek S, Matas J, Lourakis MIA, Zabulis X (2017) 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","DOI":"10.1109\/WACV.2017.103"},{"key":"11431_CR39","doi-asserted-by":"crossref","unstructured":"Hodan T, Vineet V, Gal R, Shalev E, Hanzelka J, Connell T, Urbina P, Sinha SN, Guenter BK (2019) Photorealistic image synthesis for object instance detection. In: 2019 IEEE international conference on image processing (ICIP), pp 66\u201370","DOI":"10.1109\/ICIP.2019.8803821"},{"key":"11431_CR38","doi-asserted-by":"crossref","unstructured":"Hodan T, Sundermeyer M, Drost B, Labb\u00e9 Y, Brachmann E, Michel F, Rother C, Matas J (2020) Bop challenge 2020 on 6d object localization. arXiv:abs\/2009.07378","DOI":"10.1007\/978-3-030-66096-3_39"},{"key":"11431_CR40","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1109\/LRA.2023.3245421","volume":"8","author":"Y Hu","year":"2022","unstructured":"Hu Y, Fang S, Xie W, Chen S (2022) Aerial monocular 3d object detection. IEEE Robot Autom Lett 8:1959\u20131966","journal-title":"IEEE Robot Autom Lett"},{"key":"11431_CR41","doi-asserted-by":"crossref","unstructured":"Huang R, Zheng H, Wang Y, Xia Z, Pavone M, Huang G (2024) Training an open-vocabulary monocular 3d detection model without 3d data. In: The Thirty-eighth annual conference on neural information processing systems","DOI":"10.52202\/079017-2303"},{"key":"11431_CR42","doi-asserted-by":"crossref","unstructured":"Jalal M, Spjut JB, Boudaoud B, Betke M (2019) Sidod: a synthetic image dataset for 3d object pose recognition with distractors. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 475\u2013477","DOI":"10.1109\/CVPRW.2019.00063"},{"key":"11431_CR43","doi-asserted-by":"crossref","unstructured":"Kar A, Prakash A, Liu M-Y, Cameracci E, Yuan J, Rusiniak M, Acuna D, Torralba A, Fidler S (2019) Meta-sim: learning to generate synthetic datasets. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 4550\u20134559","DOI":"10.1109\/ICCV.2019.00465"},{"key":"11431_CR44","doi-asserted-by":"crossref","unstructured":"Kaskman R, Zakharov S, Shugurov IS, Ilic S (2019) Homebreweddb: rgb-d dataset for 6d pose estimation of 3d objects. In: 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW), pp 2767\u20132776","DOI":"10.1109\/ICCVW.2019.00338"},{"key":"11431_CR46","doi-asserted-by":"crossref","unstructured":"Kehl W, Manhardt F, Tombari F, Ilic S, Navab N (2017) Ssd-6d: making rgb-based 3d detection and 6d pose estimation great again. In: 2017 IEEE international conference on computer vision (ICCV), pp 1530\u20131538","DOI":"10.1109\/ICCV.2017.169"},{"key":"11431_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592433","volume":"42","author":"B Kerbl","year":"2023","unstructured":"Kerbl B, Kopanas G, Leimkuehler T, Drettakis G (2023) 3d gaussian splatting for real-time radiance field rendering. ACM Trans Gr (TOG) 42:1\u201314","journal-title":"ACM Trans Gr (TOG)"},{"key":"11431_CR48","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3390\/electronics10040517","volume":"10","author":"S-H Kim","year":"2021","unstructured":"Kim S-H, Hwang Y (2021) A survey on deep learning based methods and datasets for monocular 3d object detection. Electronics 10:517","journal-title":"Electronics"},{"key":"11431_CR49","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y, Doll\u00e1r P, Girshick RB (2023) Segment anything. In: 2023 IEEE\/CVF international conference on computer vision (ICCV), pp 3992\u20134003","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"11431_CR50","doi-asserted-by":"crossref","unstructured":"Kleeberger K, Landgraf C, Huber MF (2019) 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","DOI":"10.1109\/IROS40897.2019.8967594"},{"key":"11431_CR51","unstructured":"Knitt M, Schyga J, Adamanov A, Hinckeldeyn J, Kreutzfeldt J (2022) Estimating the pose of a euro pallet with an rgb camera based on synthetic training data. arXiv:abs\/2210.06001"},{"key":"11431_CR52","unstructured":"Kolve E, Mottaghi R, Han W, VanderBilt E, Weihs L, Herrasti A, Deitke M, Ehsani K, Gordon D, Zhu Y, Kembhavi A, Gupta AK, Farhadi A (2017) Ai2-thor: an interactive 3d environment for visual AI. arXiv:abs\/1712.05474"},{"key":"11431_CR53","doi-asserted-by":"crossref","unstructured":"Lee T, Lee B-U, Shin I, Choe J, Shin U, Kweon I-S, Yoon K-J (2021) Uda-cope: unsupervised domain adaptation for category-level object pose estimation. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 14871\u201314880","DOI":"10.1109\/CVPR52688.2022.01447"},{"key":"11431_CR54","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1109\/LRA.2022.3149026","volume":"7","author":"X Li","year":"2022","unstructured":"Li X, Cao R, Feng Y, Chen K, Yang B, Fu C-W, Li Y, Dou Q, Liu Y, Heng P-A (2022) A sim-to-real object recognition and localization framework for industrial robotic bin picking. IEEE Robot Autom Lett 7:3961\u20133968","journal-title":"IEEE Robot Autom Lett"},{"key":"11431_CR55","unstructured":"Lian X, Yu Z, Liang R, Wang Y, Luo LR, Chen K, Zhou Y, Tang Q, Xu X, Lyu Z, Dai B, Pang J (2025) Infinite mobility: scalable high-fidelity synthesis of articulated objects via procedural generation. arXiv:abs\/2503.13424"},{"key":"11431_CR56","doi-asserted-by":"crossref","unstructured":"Lindermayr J, Odaba\u015fi C, Jordan F, Graf F, Knak L, Kraus W, Bormann R, Huber MF (2023) Ipa-3d1k: a large retail 3d model dataset for robot picking. In: 2023 IEEE\/RSJ International conference on intelligent robots and systems (IROS), pp 11404\u201311411","DOI":"10.1109\/IROS55552.2023.10342260"},{"key":"11431_CR57","doi-asserted-by":"crossref","unstructured":"Lin H, Guo Z, Zhang Y, Niu S, Li Y, Zhang R, Cui S, Li Z (2025) Drivegen: generalized and robust 3d detection in driving via controllable text-to-image diffusion generation. arXiv:abs\/2503.11122","DOI":"10.1109\/CVPR52734.2025.02561"},{"key":"11431_CR58","unstructured":"Li W, Saeedi S, McCormac J, Clark R, Tzoumanikas D, Ye Q, Huang Y, Tang R, Leutenegger S (2018) Interiornet: mega-scale multi-sensor photo-realistic indoor scenes dataset. arXiv:abs\/1809.00716"},{"key":"11431_CR61","doi-asserted-by":"crossref","unstructured":"Liu Z, Wu Z, T\u2019oth R (2020) Smoke: single-stage monocular 3d object detection via keypoint estimation. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 4289\u20134298","DOI":"10.1109\/CVPRW50498.2020.00506"},{"key":"11431_CR60","doi-asserted-by":"crossref","unstructured":"Liu X, Hao S, Xu K (2023) Pose estimation of space targets based on geometry structure features. In: Proceedings of the 2023 2nd Asia conference on algorithms, computing and machine learning","DOI":"10.1145\/3590003.3590096"},{"key":"11431_CR59","unstructured":"Liu Y, Chen W, Bai Y, Luo J-H, Song X, Jiang K, Li Z, Zhao G, Lin J, Li G, Gao W, Lin L (2024) Aligning cyber space with physical world: a comprehensive survey on embodied AI. arXiv:abs\/2407.06886"},{"key":"11431_CR62","unstructured":"Li C, Xia F, Mart\u2019in-Mart\u2019in R, Lingelbach M, Srivastava S, Shen B, Vainio K, Gokmen C, Dharan G, Jain T, Kurenkov A, Liu K, Gweon H, Wu J, Fei-Fei L, Savarese S (2021) igibson 2.0: object-centric simulation for robot learning of everyday household tasks. arXiv:abs\/2108.03272"},{"key":"11431_CR63","unstructured":"Li C, Zhang R, Wong J, Gokmen C, Srivastava S, Mart\u00edn-Mart\u00edn R, Wang C, Levine G, Lingelbach M, Sun J, Anvari M, Hwang M, Sharma M, Aydin A, Bansal D, Hunter S, Kim K-Y, Lou A, Matthews CR, Villa-Renteria I, Tang JH, Tang C, Xia F, Savarese S, Gweon H, Liu CK, Wu J, Fei-Fei L (2022) Behavior-1k: a benchmark for embodied ai with 1, 000 everyday activities and realistic simulation. In: Conference on Robot Learning. https:\/\/api.semanticscholar.org\/CorpusID:255198985"},{"key":"11431_CR64","doi-asserted-by":"crossref","unstructured":"Li X, Zhang Y, Ye X (2023) Drivingdiffusion: layout-guided multi-view driving scene video generation with latent diffusion model. arXiv:abs\/2310.07771","DOI":"10.1007\/978-3-031-73229-4_27"},{"key":"11431_CR65","unstructured":"Manhardt F, Wang G, Busam B, Nickel M, Meier S, Minciullo L, Ji X, Navab N (2020) Cps++: Improving class-level 6d pose and shape estimation from monocular images with self-supervised learning. Computer Vision and Pattern Recognition"},{"key":"11431_CR66","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1007\/s11263-023-01790-1","volume":"131","author":"J Mao","year":"2022","unstructured":"Mao J, Shi S, Wang X, Li H (2022) 3d object detection for autonomous driving: a comprehensive survey. Int J Comput Vision 131:1909\u20131963","journal-title":"Int J Comput Vision"},{"key":"11431_CR68","doi-asserted-by":"crossref","unstructured":"Martinez-Gonzalez P, Oprea S, Garcia-Garcia A, Jover-Alvarez A, Orts-Escolano S, Garcia-Rodriguez J (2018) Unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation. https:\/\/api.semanticscholar.org\/CorpusID:53111294","DOI":"10.1007\/s10055-019-00399-5"},{"key":"11431_CR67","doi-asserted-by":"crossref","unstructured":"Martinez-Gonzalez P, Oprea S, Castro-Vargas JA, Garcia-Garcia A, Orts-Escolano S, Garcia-Rodriguez J, Vincze M (2021) Unrealrox+: an improved tool for acquiring synthetic data from virtual 3d environments. In: 2021 International joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9534447"},{"key":"11431_CR69","doi-asserted-by":"crossref","unstructured":"Mata C, Locascio N, Sheikh MA, Kihara K, Fischetti DL (2022) Standardsim: a synthetic dataset for retail environments. In: International conference on image analysis and processing. https:\/\/api.semanticscholar.org\/CorpusID:246634940","DOI":"10.1007\/978-3-031-06430-2_6"},{"key":"11431_CR70","unstructured":"McCormac J, Handa A, Leutenegger S, Davison AJ (2016) Scenenet rgb-d: 5m photorealistic images of synthetic indoor trajectories with ground truth. arXiv:abs\/1612.05079"},{"key":"11431_CR71","unstructured":"Mehr G, Eskandarian A (2025) Simbev: a synthetic multi-task multi-sensor driving data generation tool and dataset. arXiv:abs\/2502.01894"},{"key":"11431_CR72","doi-asserted-by":"crossref","unstructured":"Meier J, Scalerandi L, Dhaouadi O, Kaiser J, Araslanov N, Cremers D (2024) Carla drone: monocular 3d object detection from a different perspective. arXiv:abs\/2408.11958","DOI":"10.1007\/978-3-031-85187-2_9"},{"key":"11431_CR73","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer vision - ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R (2020) Nerf: representing scenes as neural radiance fields for view synthesis. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer vision - ECCV 2020. Springer, Cham, pp 405\u2013421"},{"key":"11431_CR74","unstructured":"Morrical N, Tremblay J, Lin Y, Tyree S, Birchfield S, Pascucci V, Wald I (2021) Nvisii: a scriptable tool for photorealistic image generation. arXiv:abs\/2105.13962"},{"key":"11431_CR75","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1007\/s11263-018-1073-7","volume":"126","author":"M M\u00fcller","year":"2017","unstructured":"M\u00fcller M, Casser V, Lahoud J, Smith NG, Ghanem B (2017) Sim4cv: a photo-realistic simulator for computer vision applications. Int J Comput Vision 126:902\u2013919","journal-title":"Int J Comput Vision"},{"key":"11431_CR76","doi-asserted-by":"crossref","unstructured":"Muller R, Man Y, Celik ZB, Li MH, Gerdes RM (2022) Drivetruth: automated autonomous driving dataset generation for security applications. In: Proceedings fourth international workshop on automotive and autonomous vehicle security","DOI":"10.14722\/autosec.2022.23032"},{"key":"11431_CR77","unstructured":"NVIDIA (2022) NVIDIA Isaac Sim. https:\/\/developer.nvidia.com\/isaac\/sim. Accessed: 05, 03, 2024"},{"key":"11431_CR78","unstructured":"NVIDIA (2024): NVIDIA Omniverse. https:\/\/www.nvidia.com\/en-us\/omniverse\/"},{"issue":"9","key":"11431_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-022-10358-3","volume":"56","author":"G Paulin","year":"2023","unstructured":"Paulin G, Ivasic-Kos M (2023) Review and analysis of synthetic dataset generation methods and techniques for application in computer vision. Artif Intell Rev 56(9):1\u201345","journal-title":"Artif Intell Rev"},{"key":"11431_CR80","doi-asserted-by":"crossref","unstructured":"Prakash A, Boochoon S, Brophy M, Acuna D, Cameracci E, State G, Shapira O, Birchfield S (2018) Structured domain randomization: bridging the reality gap by context-aware synthetic data. In: 2019 International conference on robotics and automation (ICRA), pp 7249\u20137255","DOI":"10.1109\/ICRA.2019.8794443"},{"key":"11431_CR81","doi-asserted-by":"crossref","unstructured":"Proen\u00e7a PF, Gao Y (2019) Deep learning for spacecraft pose estimation from photorealistic rendering. In: 2020 IEEE international conference on robotics and automation (ICRA), pp 6007\u20136013","DOI":"10.1109\/ICRA40945.2020.9197244"},{"key":"11431_CR82","doi-asserted-by":"publisher","first-page":"2586","DOI":"10.1109\/TVCG.2023.3247087","volume":"29","author":"B Pugh","year":"2023","unstructured":"Pugh B, Chernak D, Jiddi S (2023) Geosynth: a photorealistic synthetic indoor dataset for scene understanding. IEEE Trans Visual Comput Gr 29:2586\u20132595","journal-title":"IEEE Trans Visual Comput Gr"},{"key":"11431_CR83","doi-asserted-by":"crossref","unstructured":"Qiu W, Yuille AL (2016) Unrealcv: connecting computer vision to unreal engine. arXiv:abs\/1609.01326","DOI":"10.1007\/978-3-319-49409-8_75"},{"key":"11431_CR84","doi-asserted-by":"crossref","unstructured":"Rad M, Lepetit V (2017) Bb8: a scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In: 2017 IEEE international conference on computer vision (ICCV), pp 3848\u20133856","DOI":"10.1109\/ICCV.2017.413"},{"key":"11431_CR85","doi-asserted-by":"crossref","unstructured":"Raistrick ARE, Lipson L, Ma Z, Mei L, Wang M, Zuo Y, Kayan K, Wen H, Han B, Wang Y, Newell A, Law H, Goyal A, Yang K, Deng J (2023) Infinite photorealistic worlds using procedural generation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 12630\u201312641","DOI":"10.1109\/CVPR52729.2023.01215"},{"key":"11431_CR86","doi-asserted-by":"crossref","unstructured":"Raistrick ARE, Mei L, Kayan K, Yan D, Zuo Y, Han B, Wen H, Parakh M, Alexandropoulos S, Lipson L, Ma Z, Deng J (2024) Infinigen indoors: photorealistic indoor scenes using procedural generation. In: 2024 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 21783\u201321794","DOI":"10.1109\/CVPR52733.2024.02058"},{"key":"11431_CR87","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1109\/LRA.2023.3240362","volume":"8","author":"A Remus","year":"2023","unstructured":"Remus A, D\u2019Avella S, Felice FD, Tripicchio P, Avizzano CA (2023) i2c-net: using instance-level neural networks for monocular category-level 6d pose estimation. IEEE Robot Autom Lett 8:1515\u20131522","journal-title":"IEEE Robot Autom Lett"},{"key":"11431_CR88","unstructured":"Ren T, Liu S, Zeng A, Lin J, Li K, Cao H, Chen J, Huang X, Chen Y, Yan F, Zeng Z, Zhang H, Li F, Yang J, Li H, Jiang Q, Zhang L (2024) Grounded sam: assembling open-world models for diverse visual tasks. arXiv:abs\/2401.14159"},{"key":"11431_CR89","doi-asserted-by":"crossref","unstructured":"Richter SR, Hayder Z, Koltun V (2017) Playing for benchmarks. In: 2017 IEEE International conference on computer vision (ICCV), pp 2232\u20132241","DOI":"10.1109\/ICCV.2017.243"},{"key":"11431_CR90","doi-asserted-by":"crossref","unstructured":"Roberts M, Paczan N (2020) Hypersim: a photorealistic synthetic dataset for holistic indoor scene understanding. In: 2021 IEEE\/CVF International conference on computer vision (ICCV), pp 10892\u201310902","DOI":"10.1109\/ICCV48922.2021.01073"},{"key":"11431_CR91","doi-asserted-by":"crossref","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2021) High-resolution image synthesis with latent diffusion models. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 10674\u201310685","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"11431_CR92","doi-asserted-by":"crossref","unstructured":"Rong G, Shin BH, Tabatabaee H, Lu Q, Lemke S, Mozeiko M, Boise E, Uhm G, Gerow M, Mehta S, Agafonov E, Kim TH, Sterner E, Ushiroda K, Reyes M, Zelenkovsky D, Kim S (2020) Lgsvl simulator: a high fidelity simulator for autonomous driving. In: 2020 IEEE 23rd international conference on intelligent transportation systems (ITSC), pp 1\u20136","DOI":"10.1109\/ITSC45102.2020.9294422"},{"key":"11431_CR93","doi-asserted-by":"crossref","unstructured":"Ros G, Sellart L, Materzynska J, V\u00e1zquez D, L\u00f3pez AM (2016) The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 3234\u20133243","DOI":"10.1109\/CVPR.2016.352"},{"key":"11431_CR94","unstructured":"SAE (2016) Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles"},{"key":"11431_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103907","volume":"240","author":"H Schieber","year":"2024","unstructured":"Schieber H, Demir KC, Kleinbeck C, Yang SH, Roth D (2024) Indoor synthetic data generation: a systematic review. Comput Vis Image Underst 240:103907","journal-title":"Comput Vis Image Underst"},{"key":"11431_CR96","doi-asserted-by":"crossref","unstructured":"Shah S, Dey D, Lovett C, Kapoor A (2017) Airsim: high-fidelity visual and physical simulation for autonomous vehicles. In: International Symposium on Field and Service Robotics. https:\/\/api.semanticscholar.org\/CorpusID:20999239","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"11431_CR97","doi-asserted-by":"crossref","unstructured":"Sharma S, Beierle C, D\u2019Amico S (2018) Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks. In: 2018 IEEE Aerospace Conference, pp 1\u201312","DOI":"10.1109\/AERO.2018.8396425"},{"key":"11431_CR98","unstructured":"Simoni A, Pelosin F (2025) Bounding box-guided diffusion for synthesizing industrial images and segmentation map. https:\/\/api.semanticscholar.org\/CorpusID:278339348"},{"key":"11431_CR99","doi-asserted-by":"crossref","unstructured":"Song S, Lichtenberg SP, Xiao J (2015) Sun rgb-d: a rgb-d scene understanding benchmark suite. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 567\u2013576","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"11431_CR100","doi-asserted-by":"publisher","first-page":"207635","DOI":"10.1109\/ACCESS.2020.3037724","volume":"8","author":"D Strazdas","year":"2020","unstructured":"Strazdas D, Hintz J, Fel\u00dfberg A-M, Al-Hamadi A (2020) Robots and wizards: an investigation into natural human-robot interaction. IEEE Access 8:207635\u2013207642","journal-title":"IEEE Access"},{"issue":"3","key":"11431_CR101","doi-asserted-by":"publisher","first-page":"923","DOI":"10.3390\/s22030923","volume":"22","author":"D Strazdas","year":"2022","unstructured":"Strazdas D, Hintz J, Khalifa A, Abdelrahman AA, Hempel T, Al-Hamadi A (2022) Robot system assistant (Rosa): towards intuitive multi-modal and multi-device human-robot interaction. Sens 22(3):923","journal-title":"Sens"},{"key":"11431_CR102","doi-asserted-by":"crossref","unstructured":"Sun T, Segu M, Postels J, Wang Y, Gool LV, Schiele B, Tombari F, Yu F (2022) Shift: a synthetic driving dataset for continuous multi-task domain adaptation. In: 2022 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 21339\u201321350","DOI":"10.1109\/CVPR52688.2022.02068"},{"key":"11431_CR103","doi-asserted-by":"crossref","unstructured":"Sural S, Sahu N, Rajkumar R (2024) Contextualfusion: context-based multi-sensor fusion for 3d object detection in adverse operating conditions. In: 2024 IEEE intelligent vehicles symposium (IV), pp 1534\u20131541","DOI":"10.1109\/IV55156.2024.10588584"},{"key":"11431_CR105","unstructured":"Tang J, Ren J, Zhou H, Liu Z, Zeng G (2023a) Dreamgaussian: generative gaussian splatting for efficient 3d content creation. arXiv:abs\/2309.16653"},{"key":"11431_CR107","doi-asserted-by":"crossref","unstructured":"Tang J, Zhou H, Chen X, Hu T, Ding E, Wang J, Zeng G (2023b) Delicate textured mesh recovery from nerf via adaptive surface refinement. In: 2023 IEEE\/CVF international conference on computer vision (ICCV), pp 17693\u201317703","DOI":"10.1109\/ICCV51070.2023.01626"},{"key":"11431_CR104","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/LRA.2023.3331624","volume":"9","author":"J Tang","year":"2024","unstructured":"Tang J, Chen Z, Fu B, Lu W, Li S, Li X, Ji X (2024a) Rov6d: 6d pose estimation benchmark dataset for underwater remotely operated vehicles. IEEE Robot Autom Lett 9:65\u201372","journal-title":"IEEE Robot Autom Lett"},{"key":"11431_CR106","doi-asserted-by":"crossref","unstructured":"Tang Y, Tai C, Chen F-X, Zhang W, Zhang T, Liu X-P, Liu Y-J, Zeng L (2024b) Mobile robot oriented large-scale indoor dataset for dynamic scene understanding. In: 2024 IEEE international conference on robotics and automation (ICRA), pp 613\u2013620","DOI":"10.1109\/ICRA57147.2024.10611489"},{"key":"11431_CR108","doi-asserted-by":"crossref","unstructured":"Teufel S, Gamerdinger J, Kirchner J-P, Volk G, Bringmann O (2024) Collective perception datasets for autonomous driving: a comprehensive review. In: 2024 IEEE intelligent vehicles symposium (IV), pp 1548\u20131555","DOI":"10.1109\/IV55156.2024.10588475"},{"key":"11431_CR109","doi-asserted-by":"crossref","unstructured":"Tonderski A, Lindstr\u00f6m C, Hess G, Ljungbergh W, Svensson L, Petersson C (2023) Neurad: neural rendering for autonomous driving. In: 2024 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 14895\u201314904","DOI":"10.1109\/CVPR52733.2024.01411"},{"key":"11431_CR111","doi-asserted-by":"crossref","unstructured":"Tong W, Xie J, Li T, Deng H, Geng X, Zhou R, Yang D, Dai B, Lu L, Li H (2023) 3d data augmentation for driving scenes on camera. arXiv:abs\/2303.10340","DOI":"10.1007\/978-981-97-8508-7_4"},{"key":"11431_CR110","doi-asserted-by":"crossref","unstructured":"Tong S, Ouyang Z, Hu Q, Li D (2024) 3d spacecraft position and orientation estimation model based on monocular image. In: 2024 IEEE international conference on cybernetics and intelligent systems (CIS) and IEEE international conference on robotics, automation and mechatronics (RAM), pp 508\u2013513","DOI":"10.1109\/CIS-RAM61939.2024.10672786"},{"key":"11431_CR112","unstructured":"To T, Tremblay J, McKay D, Yamaguchi Y, Leung K, Balanon A, Cheng J, Hodge W, Birchfield S (2018) NDDS: NVIDIA deep learning dataset synthesizer. https:\/\/github.com\/NVIDIA\/Dataset_Synthesizer"},{"key":"11431_CR113","doi-asserted-by":"crossref","unstructured":"Tran QH, Choate JA, Taylor CN, Nykl SL, Curtis DH (2023) Monocular vision and machine learning for pose estimation. In: 2023 IEEE\/ION Position, Location and Navigation Symposium (PLANS), pp 128\u2013136","DOI":"10.1109\/PLANS53410.2023.10140128"},{"key":"11431_CR114","doi-asserted-by":"crossref","unstructured":"Tremblay J, To T, Birchfield S (2018a) 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), pp 2119\u201321193","DOI":"10.1109\/CVPRW.2018.00275"},{"key":"11431_CR115","unstructured":"Tremblay J, To T, Sundaralingam B, Xiang Y, Fox D, Birchfield S (2018b) Deep object pose estimation for semantic robotic grasping of household objects. arXiv:abs\/1809.10790"},{"key":"11431_CR116","unstructured":"T\u00fcrkcan MK, Li Y, Zang C, Ghaderi J, Zussman G, Kostic Z (2024) Boundless: generating photorealistic synthetic data for object detection in urban streetscapes. arXiv:abs\/2409.03022"},{"key":"11431_CR117","doi-asserted-by":"crossref","unstructured":"Tyree S, Tremblay J, To T, Cheng J, Mosier T, Smith J, Birchfield S (2022) 6-dof pose estimation of household objects for robotic manipulation: an accessible dataset and benchmark. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 13081\u201313088","DOI":"10.1109\/IROS47612.2022.9981838"},{"key":"11431_CR118","unstructured":"Unity Technologies (2024) : Unity. Game development platform. https:\/\/unity.com\/"},{"key":"11431_CR120","doi-asserted-by":"crossref","unstructured":"Wang M, Deng W (2018) Deep visual domain adaptation: a survey. arXiv:abs\/1802.03601","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"11431_CR123","doi-asserted-by":"crossref","unstructured":"Wang H, Sridhar S, Huang J, Valentin JPC, Song S, Guibas LJ (2019) Normalized object coordinate space for category-level 6d object pose and size estimation. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 2637\u20132646","DOI":"10.1109\/CVPR.2019.00275"},{"key":"11431_CR122","doi-asserted-by":"crossref","unstructured":"Wang G, Manhardt F, Shao J, Ji X, Navab N, Tombari F (2020) Self6d: self-supervised monocular 6d object pose estimation. arXiv:abs\/2004.06468","DOI":"10.1007\/978-3-030-58452-8_7"},{"key":"11431_CR119","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102077","volume":"70","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang C, Long P, Gu Y, Li W (2021) Recent advances in 3d object detection based on rgb-d: a survey. Displays 70:102077","journal-title":"Displays"},{"key":"11431_CR121","doi-asserted-by":"crossref","unstructured":"Wang P, Jung H, Li Y, Shen S, Srikanth RP, Garattoni L, Meier S, Navab N, Busam B (2022) Phocal: a multi-modal dataset for category-level object pose estimation with photometrically challenging objects. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 21190\u201321199","DOI":"10.1109\/CVPR52688.2022.02054"},{"key":"11431_CR124","doi-asserted-by":"crossref","unstructured":"Wang X, Zhu Z, Huang G, Chen X, Zhu J, Lu J (2024) Drivedreamer: towards real-world-drive world models for autonomous driving. In: European conference on computer vision. https:\/\/api.semanticscholar.org\/CorpusID:274611109","DOI":"10.1007\/978-3-031-73195-2_4"},{"key":"11431_CR125","unstructured":"Wrenninge M, Unger J (2018) Synscapes: a photorealistic synthetic dataset for street scene parsing. arXiv:abs\/1810.08705"},{"key":"11431_CR126","doi-asserted-by":"crossref","unstructured":"Wu Z, Liu T, Luo L, Zhong Z, Chen J, Xiao H, Hou C, Lou H, Chen Y-H, Yang R, Huang Y, Ye X, Yan Z, Shi Y, Liao Y, Zhao H (2023a) Mars: an instance-aware, modular and realistic simulator for autonomous driving. arXiv:abs\/2307.15058","DOI":"10.1007\/978-981-99-8850-1_1"},{"key":"11431_CR127","unstructured":"Wu W, Zhao Y, Chen H, Gu Y, Zhao R, He Y, Zhou H, Shou MZ, Shen C (2023b) Datasetdm: synthesizing data with perception annotations using diffusion models. arXiv:abs\/2308.06160"},{"key":"11431_CR128","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/LRA.2020.2965078","volume":"5","author":"F Xia","year":"2019","unstructured":"Xia F, Shen BW, Li C, Kasimbeg P, Tchapmi ME, Toshev A, Mart\u00edn-Mart\u00edn R, Savarese S (2019) Interactive Gibson benchmark: a benchmark for interactive navigation in cluttered environments. IEEE Robot Autom Lett 5:713\u2013720","journal-title":"IEEE Robot Autom Lett"},{"key":"11431_CR129","doi-asserted-by":"crossref","unstructured":"Xiang J, Yang J, Huang B, Tong X (2023) 3d-aware image generation using 2d diffusion models. In: 2023 IEEE\/CVF international conference on computer vision (ICCV), pp 2383\u20132393","DOI":"10.1109\/ICCV51070.2023.00226"},{"key":"11431_CR130","doi-asserted-by":"crossref","unstructured":"Xie Z, Liu Z, Peng Z, Wu W, Zhou B (2025) Vid2sim: realistic and interactive simulation from video for urban navigation. arXiv:abs\/2501.06693","DOI":"10.1109\/CVPR52734.2025.00155"},{"key":"11431_CR131","doi-asserted-by":"crossref","unstructured":"Xu A, Vasileva MI, Dave A, Seshadri A (2022) Handsoff: labeled dataset generation with no additional human annotations. In: 2023 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7991\u20138000","DOI":"10.1109\/CVPR52729.2023.00772"},{"key":"11431_CR132","doi-asserted-by":"crossref","unstructured":"Yang Z, Zeng A, Yuan C, Li Y (2023a) Effective whole-body pose estimation with two-stages distillation. In: 2023 IEEE\/CVF international conference on computer vision workshops (ICCVW), pp 4212\u20134222","DOI":"10.1109\/ICCVW60793.2023.00455"},{"key":"11431_CR133","unstructured":"Yang Z, Zhan F, Liu K, Xu M, Lu S (2023b) Ai-generated images as data source: the dawn of synthetic era. arXiv:abs\/2310.01830"},{"key":"11431_CR134","unstructured":"Yi T, Fang J, Zhou Z, Wang J, Wu G, Xie L, Zhang X, Liu W, Wang X, Tian Q (2024) Gaussiandreamerpro: text to manipulable 3D Gaussians with highly enhanced quality. arXiv:abs\/2406.18462"},{"key":"11431_CR135","unstructured":"Zanjani FG, Abati D, Wiggers AJ, Kalatzis D, Petersen J, Cai H, Habibian A (2025) Gaussian splatting is an effective data generator for 3d object detection. arXiv:abs\/2504.16740"},{"key":"11431_CR136","doi-asserted-by":"crossref","unstructured":"Zhang L, Rao A, Agrawala M (2023) Adding conditional control to text-to-image diffusion models. In: 2023 IEEE\/CVF international conference on computer vision (ICCV), pp 3813\u20133824","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"11431_CR138","doi-asserted-by":"crossref","unstructured":"Zhou L, Song Y, Gao Y, Yu Z, Sodamin M, Liu H, Ma L, Liu L, Liu H, Liu Y, Li H, Chen G, Knoll A (2023) Garchingsim: an autonomous driving simulator with photorealistic scenes and minimalist workflow. In: 2023 IEEE 26th international conference on intelligent transportation systems (ITSC), pp 4227\u20134232","DOI":"10.1109\/ITSC57777.2023.10421839"},{"key":"11431_CR137","unstructured":"Zhou Y, Simon M, Peng Z, Mo S, Zhu H, Guo M, Zhou B (2024) Simgen: simulator-conditioned driving scene generation. arXiv:abs\/2406.09386"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11431-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11431-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11431-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T03:10:28Z","timestamp":1769483428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11431-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":137,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["11431"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11431-3","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,13]]},"assertion":[{"value":"22 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"8"}}