{"id":13994353,"url":"https://github.com/unrealcv/synthetic-computer-vision","last_synced_at":"2025-05-16T10:06:27.344Z","repository":{"id":40637007,"uuid":"65410692","full_name":"unrealcv/synthetic-computer-vision","owner":"unrealcv","description":"A list of synthetic dataset and tools for computer vision","archived":false,"fork":false,"pushed_at":"2023-04-15T08:46:28.000Z","size":135,"stargazers_count":1015,"open_issues_count":3,"forks_count":180,"subscribers_count":79,"default_branch":"master","last_synced_at":"2025-04-09T04:06:39.050Z","etag":null,"topics":["computer-vision","dataset","synthetic-images","virtual-worlds"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/unrealcv.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-08-10T19:31:52.000Z","updated_at":"2025-04-01T01:35:57.000Z","dependencies_parsed_at":"2022-08-02T16:00:55.723Z","dependency_job_id":"4260f8f1-52dd-4f04-8899-0ed1c53b2659","html_url":"https://github.com/unrealcv/synthetic-computer-vision","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unrealcv%2Fsynthetic-computer-vision","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unrealcv%2Fsynthetic-computer-vision/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unrealcv%2Fsynthetic-computer-vision/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unrealcv%2Fsynthetic-computer-vision/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/unrealcv","download_url":"https://codeload.github.com/unrealcv/synthetic-computer-vision/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254509475,"owners_count":22082891,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","dataset","synthetic-images","virtual-worlds"],"created_at":"2024-08-09T14:02:49.786Z","updated_at":"2025-05-16T10:06:27.297Z","avatar_url":"https://github.com/unrealcv.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# Synthetic for Computer Vision\n\nThis is a repo for tracking the progress of using synthetic images for computer vision research. If you found any important work is missing or information is not up-to-date, please edit this file directly and make a pull request. Each publication is tagged with a keyword to make it easier to search.\n\nIf you find anything missing from this page, please edit this `README.md` file to add it. When adding a new item, you can simply follow the format of existing items. How this document is structured is documented in [`contribute.md`](contribute.md).\n\n\n**How to use**: Click publication to jump to the paper title, detailed information such as code and project page will be provided together with pdf file.**\n\n\u003cdiv id=\"dataset\"\u003e\u003c/div\u003e\n\n## Synthetic image dataset\n\n\n- [SunCG (Princeton)](https://sscnet.cs.princeton.edu/)\n- [Minos](https://minosworld.github.io/)\n- [House3d (Facebook)](https://github.com/facebookresearch/House3D)\n- [Procedural Human Action Videos (PHAV)](#de2016procedural)\n- [SURREAL](#varol2017learning)\n- [Virtual KITTI](#gaidon2016virtual)      \n- [Synthia](#ros2016synthia)         \n- [Sintel](#butler2012naturalistic), A synthetic dataset for optical flow\n- [SceneFlow](#mayer2015large)\n- [4D Light Fields](#honauer2016dataset)\n- [ICL-NUIM dataset](#handa2014benchmark)\n- [Driving in the Matrix](#drivingthematrix)\n- [Playing for Benchmarks](http://playing-for-benchmarks.org/overview/)\n\n\u003cdiv id=\"models\"\u003e\u003c/div\u003e\n\n## 3D Model Repository\n\nRealistic 3D models are critical for creating realistic and diverse virtual worlds. Here are research efforts for creating 3D model repositories.\n\n- [ShapeNet](#chang2015shapenet)  \n- [3dscan](#choi2016large)      \n- [seeing3Dchairs](#aubry2014seeing)\n\n\u003cdiv id=\"tool\"\u003e\u003c/div\u003e\n\n## Tools\n\n- [AIPlayground: UE4 Based Data Ablation tool](#mousavi2020ai), see [project page](https://github.com/MMehdiMousavi/AIP)\n- [AirSim (Microsoft)](https://github.com/Microsoft/AirSim)\n- [CARLA (Intel)](https://github.com/carla-simulator/carla)\n- [Unity ML agents](https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/)\n- Render SMPL human bodies on Blender, see [CVPR2017](#varol2017learning)\n- Render for CNN, based on Blender, see [ICCV2015](#su2015render)\n- [UETorch](https://github.com/facebook/UETorch), based on UE4, see [ICML2016](#lerer2016learning)\n- [UnrealCV](https://github.com/unrealcv/unrealcv), based on UE4, see [ArXiv](#qiu2016unrealcv)\n- VizDoom, based on Doom, see [ArXiv](#kempka2016vizdoom)\n- OpenAI Universe, see [project page](https://universe.openai.com/)\n- Blender addon for 4D light field rendering, see [project page](https://github.com/lightfield-analysis/blender-addon)\n- Event-Camera Dataset and Simulator see [project page](https://github.com/uzh-rpg/rpg_davis_simulator)\n- [NVIDIA Deep learning Dataset Synthesizer (NDDS)](https://github.com/NVIDIA/Dataset_Synthesizer)\n\n\u003cdiv id=\"resource\"\u003e\u003c/div\u003e\n\n## Resources\n\n[ECCV 2016 Workshop Virtual/Augmented Reality for Visual Artificial Intelligence (VARVAI) workshop](http://adas.cvc.uab.es/varvai2016/)\n\n[ICCV 2017 Workshop Role of Simulation in Computer Vision](https://www.microsoft.com/en-us/research/event/iccv-2017-role-of-simulation-in-computer-vision/)\n\n[Virtual Reality Meets Physical Reality:\nModelling and Simulating Virtual Humans and Environments\nSiggraph Asia 2016 workshop](http://sigvr.org/)\n\n[CVPR 2017 Workshop THOR Challenge](http://vuchallenge.org/thor.html)\n\nSee also: http://riemenschneider.hayko.at/vision/dataset/index.php?filter=+synthetic\n\n## Misc.\n- RealismCNN [github](https://github.com/junyanz/RealismCNN)\n- Abnormality Detection in Images(http://paul.rutgers.edu/~babaks/abnormality_detection.html)\n\n\u003cdiv id=\"reference\"\u003e\u003c/div\u003e\n\n## Reference\n\n\u003c!-- The div id is bib citekey from google scholar, use div id makes it easier to reference a work in this document. --\u003e\n\n### 2020\n\n\u003cdiv id=\"mousavi2020ai\"\u003e\u003c/div\u003e\n\n-  Mousavi, Mehdi and Khanal, Aashis and Estrada, Rolando. \"AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning\" International Symposium on Visual Computing (ISVC), 2020.\n\t ([pdf](https://arxiv.org/abs/2007.06153))\n\t ([project](https://github.com/MMehdiMousavi/AIP))\n\t\n\n### 2017\n\n(Total=12)\n\n- Adversarially Tuned Scene Generation\n\t([pdf](https://arxiv.org/pdf/1701.00405.pdf))\n\t\n- UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications\n\t([pdf](https://arxiv.org/abs/1708.05869))\n\t([project](https://ue4sim.org/))\n\n\u003cdiv id=\"richterplaying\"\u003e\u003c/div\u003e\n\n- Playing for Benchmarks\n\t([pdf](http://vladlen.info/papers/playing-for-benchmarks.pdf))\n\n\u003cdiv id=\"mitash2017self\"\u003e\u003c/div\u003e\n\n- A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation\n\t\u003cspan class=\"octicon octicon-mark-github\"\u003e\u003c/span\u003e\n\t([:octocat:code](https://github.com/cmitash/PHYSIM_6DPose))\n\t([pdf](https://arxiv.org/pdf/1703.03347.pdf))\n\t([project](http://paul.rutgers.edu/~cm1074/PHYSIM.html))\n\t\n\u003cdiv id=\"de2016procedural\"\u003e\u003c/div\u003e\n\t\n - Procedural Generation of Videos to Train Deep Action Recognition Networks\n\t([pdf](http://openaccess.thecvf.com/content_cvpr_2017/papers/de_Souza_Procedural_Generation_of_CVPR_2017_paper.pdf))\n\t([project](http://adas.cvc.uab.es/phav/))\n\t([citation:8](https://scholar.google.com/scholar?cites=12002008688864745159\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"varol2017learning\"\u003e\u003c/div\u003e\n\n- Learning from Synthetic Humans\n\t\u003cspan class=\"octicon octicon-mark-github\"\u003e\u003c/span\u003e\n\t([:octocat:code](https://github.com/gulvarol/surreal))\n\t([pdf](https://arxiv.org/abs/1701.01370))\n\t([project](http://www.di.ens.fr/willow/research/surreal/))\n\ttag: synthetic human\n\t\n- [Nvidia Issac](http://www.marketwired.com/press-release/nvidia-ushers-new-era-robotics-with-breakthroughs-making-it-easier-build-train-intelligent-2215481.htm)\n\n- Configurable, Photorealistic Image Rendering and Ground Truth Synthesis by Sampling Stochastic Grammars Representing Indoor Scenes\n\n\u003cdiv id=\"airsim\"\u003e\u003c/div\u003e\n\n- Aerial Informatics and Robotics Platform\n\t\u003cspan class=\"octicon octicon-mark-github\"\u003e\u003c/span\u003e\n\t([:octocat:code](https://github.com/Microsoft/AirSim))\n\t([pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/02/aerial-informatics-robotics-TR.pdf))\n\t([project](https://www.microsoft.com/en-us/research/project/aerial-informatics-robotics-platform/))\n\ttag: tool\n\n\n\u003cdiv id=\"tobin2017domain\"\u003e\u003c/div\u003e\n\n- Tobin, Josh, et al. \"Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World.\" arXiv preprint arXiv:1703.06907 (2017). tag: domain\n\t([pdf](https://arxiv.org/pdf/1703.06907.pdf))\n\t\n\u003cdiv id=\"drivingthematrix\"\u003e\u003c/div\u003e\n\n- M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, Karl Rosaen,and R. Vasudevan, “Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?,” in IEEE International Conference on Robotics and Automation, pp. 1–8, 2017.\n\t\u003cspan class=\"octicon octicon-mark-github\"\u003e\u003c/span\u003e\n\t([:octocat:code](https://github.com/umautobots/driving-in-the-matrix))\n\t([pdf](https://arxiv.org/pdf/1610.01983))\n\t([project](https://fcav.engin.umich.edu/sim-dataset/))\n\t([citation:3](https://scholar.google.com/scholar?um=1\u0026ie=UTF-8\u0026lr\u0026cites=2191650018344815319))\n\t\n\u003cdiv id=\"person re-ID\"\u003e\u003c/div\u003e\n\n- Zheng Z, Zheng L, Yang Y. \"Unlabeled samples generated by gan improve the person re-identification baseline in vitro\" in Proceedings of IEEE International Conference on Computer Vision, 2017.\n        \u003cspan class=\"octicon octicon-mark-github\"\u003e\u003c/span\u003e\n\t([:octocat:code](https://github.com/layumi/Person-reID_GAN))\n\t([pdf](https://arxiv.org/abs/1701.07717))\n\t([citation:48](https://scholar.google.com/scholar?oi=bibs\u0026hl=zh-CN\u0026cites=270746001988088124)) \n\ttag: generated images by GAN\n\n### 2016\n(Total=17)\n\n\u003cdiv id=\"sadeghi2016rl\"\u003e\u003c/div\u003e\n\n- Sadeghi, Fereshteh, and Sergey Levine. \"rl: Real single-image flight without a single real image. arXiv preprint.\" arXiv preprint arXiv:1611.04201 12 (2016). tag: rl\n\n- Johnson, Justin, et al. \"CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning.\" arXiv preprint arXiv:1612.06890 (2016).\n\t([pdf](https://arxiv.org/abs/1612.06890))\n\n- McCormac, John, et al. \"SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth.\" arXiv preprint arXiv:1612.05079 (2016).\n\n- de Souza, César Roberto, et al. \"Procedural Generation of Videos to Train Deep Action Recognition Networks.\" arXiv preprint arXiv:1612.00881 (2016).\n\t([pdf](https://arxiv.org/abs/1612.00881))\n\t([project](http://adas.cvc.uab.es/phav/))\n\ttag: synthetic human\n\n- Synnaeve, Gabriel, et al. \"TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games.\" arXiv preprint arXiv:1611.00625 (2016).\n\t([pdf](https://arxiv.org/abs/1611.00625))\n\t([code](https://github.com/TorchCraft/TorchCraft))\n\n- Lin, Jenny, et al. \"A virtual reality platform for dynamic human-scene interaction.\" SIGGRAPH ASIA 2016 Virtual Reality meets Physical Reality: Modelling and Simulating Virtual Humans and Environments. ACM, 2016.\n\t([pdf](https://xiaozhuchacha.github.io/projects/siggraphasia16_vrplatform/vrplatform2016siggraphasia.pdf))\n\t([project](https://xiaozhuchacha.github.io/projects/siggraphasia16_vrplatform/index.html))\n\n- Mahendran, A., et al. \"ResearchDoom and CocoDoom: Learning Computer Vision with Games.\" arXiv preprint arXiv:1610.02431 (2016).\n\t([pdf](https://arxiv.org/pdf/1610.02431.pdf))\n\t([project](www.robots.ox.ac.uk/~vgg/research/researchdoom/))\n\n\u003cdiv id=\"ros2016synthia\"\u003e\u003c/div\u003e\n\n- The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes.  2016\n\t ([pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Ros_The_SYNTHIA_Dataset_CVPR_2016_paper.html))\n\t ([project](http://synthia-dataset.net/))\n\t ([citation:4](http://scholar.google.com/scholar?cites=9178628328030932213\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"gaidon2016virtual\"\u003e\u003c/div\u003e\n\n-   Virtual Worlds as Proxy for Multi-Object Tracking Analysis.  2016   \n\t ([pdf](http://arxiv.org/abs/1605.06457))\n\t ([project](http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds))\n\t ([citation:5](http://scholar.google.com/scholar?cites=11727455440906017188\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n-   Playing for data: Ground truth from computer games.  2016   \n\t ([pdf](http://link.springer.com/chapter/10.1007/978-3-319-46475-6_7))\n\t ([citation:1](http://scholar.google.com/scholar?cites=12822958035144353200\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n-   Play and Learn: Using Video Games to Train Computer Vision Models.  2016   \n\t ([pdf](http://arxiv.org/abs/1608.01745))\n\t ([citation:1](http://scholar.google.com/scholar?cites=16081073673799361643\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n-   ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning.  2016    \n\t([:octocat:code](https://github.com/Marqt/ViZDoom))\n\t([pdf](http://arxiv.org/abs/1605.02097))\n\t([project](http://vizdoom.cs.put.edu.pl/))\n\t([citation:4](http://scholar.google.com/scholar?cites=4101579648300742816\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"choi2016large\"\u003e\u003c/div\u003e\n\n-   A large dataset of object scans.  2016   \n\t ([pdf](http://arxiv.org/abs/1602.02481))\n\t ([project](http://redwood-data.org/3dscan/))\n\t ([citation:6](http://scholar.google.com/scholar?cites=5989950372336055491\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"qiu2016unrealcv\"\u003e\u003c/div\u003e\n\n-   UnrealCV: Connecting Computer Vision to Unreal Engine  2016    \n\t\u003cspan class=\"octicon octicon-mark-github\"\u003e\u003c/span\u003e\n\t([:octocat:code](https://github.com/unrealcv/unrealcv))\n\t([project](http://unrealcv.github.io))\n\t([pdf](http://arxiv.org/abs/1609.01326))   \n\n\u003cdiv id=\"lerer2016learning\"\u003e\u003c/div\u003e\n\n-   Learning Physical Intuition of Block Towers by Example  2016   \n\t([:octocat:code](https://github.com/facebook/UETorch))\n\t([pdf](http://arxiv.org/abs/1603.01312))\n\t([citation:12](http://scholar.google.com/scholar?cites=12846348306706460250\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n-   Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning  2016   \n\t ([pdf](http://arxiv.org/abs/1609.05143))   \n\n\u003cdiv id=\"honauer2016dataset\"\u003e\u003c/div\u003e\n\n-   A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields. ACCV 2016   \n\t ([:octocat:code](https://github.com/lightfield-analysis))\n\t ([pdf](http://lightfield-analysis.net/benchmark/paper/lightfield_benchmark_accv_2016.pdf))\n\t ([project](http://lightfield-analysis.net/))\n\t ([citation](https://scholar.google.de/scholar?cluster=3369030498099069181\u0026hl=en\u0026as_sdt=0,5))   \n\n### 2015\n(Total=3)\n\n-   A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation.  2015    \n\t ([pdf](http://arxiv.org/abs/1512.02134))\n\t ([citation:9](http://scholar.google.com/scholar?cites=16431759299155441580\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"su2015render\"\u003e\u003c/div\u003e\n\n-   Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views.  2015   \n\t([:octocat:code](https://github.com/ShapeNet/RenderForCNN))\n\t([pdf](http://www.cv-foundation.org/openaccess/content_iccv_2015/html/Su_Render_for_CNN_ICCV_2015_paper.html))\n\t([citation:33](http://scholar.google.com/scholar?cites=1209553997502402606\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"chang2015shapenet\"\u003e\u003c/div\u003e\n\n-   Shapenet: An information-rich 3d model repository.  2015    \n\t ([pdf](http://arxiv.org/abs/1512.03012))\n\t ([project](http://shapenet.cs.stanford.edu/))\n\t ([citation:27](http://scholar.google.com/scholar?cites=1341601736562194564\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n### 2014\n(Total=2)\n\n-   Virtual and real world adaptation for pedestrian detection.  2014    \n\t ([pdf](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6587038))\n\t ([citation:46](http://scholar.google.com/scholar?cites=2637402509859183337\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n\u003cdiv id=\"aubry2014seeing\"\u003e\u003c/div\u003e\n\n-   Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models.  2014   \n\t([:octocat:code](https://github.com/dimatura/seeing3d))\n\t([pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Aubry_Seeing_3D_Chairs_2014_CVPR_paper.html))\n\t([project](http://www.di.ens.fr/willow/research/seeing3Dchairs/))\n\t([citation:110](http://scholar.google.com/scholar?cites=18030645502969108287\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\t\n\u003cdiv id=\"handa2014benchmark\"\u003e\u003c/div\u003e\n\n- Handa, Ankur, Thomas Whelan, John McDonald, and Andrew J. Davison. \"A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM.\" In Robotics and automation (ICRA), 2014 IEEE international conference on, pp. 1524-1531. IEEE, 2014.\n\t([project](https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html))\n\n### 2013\n(Total=1)\n\n-   Detailed 3d representations for object recognition and modeling.  2013   \n\t ([pdf](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6516504))\n\t ([citation:67](http://scholar.google.com/scholar?cites=6595507135181144034\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n### 2012\n(Total=1)\n\n\u003cdiv id=\"butler2012naturalistic\"\u003e\u003c/div\u003e\n\n-   A naturalistic open source movie for optical flow evaluation.  2012    \n\t ([pdf](http://link.springer.com/chapter/10.1007/978-3-642-33783-3_44))\n\t ([project](http://sintel.is.tue.mpg.de/))\n\t ([citation:227](http://scholar.google.com/scholar?cites=15124407213489971559\u0026as_sdt=20000005\u0026sciodt=0,21\u0026hl=en))\n\n### 2010\n(Total=1)\n\n-   Learning appearance in virtual scenarios for pedestrian detection.  2010   \n\t ([pdf](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5540218))\n\t ([citation:79](http://scholar.google.com/scholar?cites=17243485674852907889\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n\n### 2007\n(Total=1)\n\n-   Ovvv: Using virtual worlds to design and evaluate surveillance systems.  2007   \n\t ([pdf](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270516))\n\t ([citation:58](http://scholar.google.com/scholar?cites=3459961090644684583\u0026as_sdt=2005\u0026sciodt=0,5\u0026hl=en))\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Funrealcv%2Fsynthetic-computer-vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Funrealcv%2Fsynthetic-computer-vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Funrealcv%2Fsynthetic-computer-vision/lists"}