Fetching the paper…
Reading the bibliography…
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.
1911
Earlier work this paper cites.
W. Jakob, “Mitsuba renderer,” 2010. [Online]. Available: http://www.mitsuba-renderer.org
2010
Earlier work this paper cites.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cremers, and T. Brox, “Flownet: Learning optical flow with convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2758–2766
2015
Earlier work this paper cites.
E. Wood, T. Baltrusaitis, X. Zhang, Y. Sugano, P. Robinson, and A. Bulling, “Rendering of eyes for eye-shape registration and gaze estimation,” in Proceedings of the ICCV , 2015, pp. 3756–3764
2015
Earlier work this paper cites.
Y. Movshovitz-Attias, T. Kanade, and Y. Sheikh, “How useful is photo-realistic rendering for visual learning?” in European Conference on Computer Vision . Springer, 2016, pp. 202–217
2016
Earlier work this paper cites.
E. Wood, T. Baltrušaitis, L.-P. Morency, P. Robinson, and A. Bulling, “Learning an appearance-based gaze estimator from one million synthesised images,” in Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications . ACM, 2016, pp. 131–138
2016
Cited alongside, same era.
Y. Zhang, S. Song, E. Yumer, M. Savva, J.-Y. Lee, H. Jin, and T. Funkhouser, “Physically-based rendering for indoor scene understanding using convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5287–5295
2017
Cited alongside, same era.
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from simulated and unsupervised images through adversarial training,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2107–2116
2017
Cited alongside, same era.
Z. Li and N. Snavely, “Cgintrinsics: Better intrinsic image decomposition through physically-based rendering,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 371–387
2018
Later among the works it cites.
B. O. Community, Blender - a 3D modelling and rendering package , Blender Foundation, Stichting Blender Foundation, Amsterdam, 2019. [Online]. Available: http://www.blender.org
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
S. Hinterstoisser, V. Lepetit, P. Wohlhart, and K. Konolige, “On pre-trained image features and synthetic images for deep learning,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 0–0
2018
Cited alongside, same era.
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser, “Semantic scene completion from a single depth image.”
Cited in the paper.
H. Su, C. R. Qi, Y. Li, and L. Guibas, “Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views.”
Cited in the paper.
M. Sundermeyer, Z.-C. Marton, M. Durner, M. Brucker, and R. Triebel, “Implicit 3d orientation learning for 6d object detection from rgb images.”
Cited in the paper.