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Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated.
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on. vol. 3, pp. 2149–2154. IEEE (2004)
2004
Earlier work this paper cites.
Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: Usarsim: a robot simulator for research and education. In: Proceedings 2007 IEEE International Conference on Robotics and Automation. pp. 1400–1405. IEEE (2007)
2007
Earlier work this paper cites.
Taylor, G.R., Chosak, A.J., Brewer, P.C.: Ovvv: Using virtual worlds to design and evaluate surveillance systems. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8. IEEE (2007)
2007
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. pp. 248–255. IEEE (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88(2), 303–338 (2010)
2010
Earlier work this paper cites.
Marin, J., Vázquez, D., Gerónimo, D., López, A.M.: Learning appearance in virtual scenarios for pedestrian detection. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. pp. 137–144. IEEE (2010)
2010
Earlier work this paper cites.
Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: European Conference on Computer Vision. pp. 611–625. Springer (2012)
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)
2012
Cited alongside, same era.
Todorov, E., Erez, T., Tassa, Y.: Mujoco: A physics engine for model-based control. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 5026–5033. IEEE (2012)
2012
Cited alongside, same era.
Battaglia, P.W., Hamrick, J.B., Tenenbaum, J.B.: Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences 110(45), 18327–18332 (2013)
2013
Cited alongside, same era.
2014
Cited alongside, same era.
Hattori, H., Naresh Boddeti, V., Kitani, K.M., Kanade, T.: Learning scene-specific pedestrian detectors without real data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3819–3827 (2015)
2015
Later among the works it cites.
Peng, X., Sun, B., Ali, K., Saenko, K.: Learning deep object detectors from 3d models. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1278–1286 (2015)
2015
Later among the works it cites.
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2686–2694 (2015)
2015
Later among the works it cites.
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2015
Cited alongside, same era.
Chen, C., Seff, A., Kornhauser, A., Xiao, J.: Deepdriving: Learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2722–2730 (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2016
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2016
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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. 3234–3243 (2016)
2016
Closest in time.