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A. Kar, A. Prakash, M. Liu, E. Cameracci, J. Yuan, M. Rusiniak, D. Acuna, A. Torralba, and S. Fidler, “Meta-sim: Learning to generate synthetic datasets,” 2019. [Online]. Available: http://arxiv.org/abs/1904.11621
Original
1904
Earlier work this paper cites.
J. Hong, B. Sapp, and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,” CVPR , 2019. [Online]. Available: http://arxiv.org/abs/1906.08945
Original
1906
Earlier work this paper cites.
X. Yang, Q. Liu, J. Yan, and A. Li, “R3det: Refined single-stage detector with feature refinement for rotating object,” 2019. [Online]. Available: http://arxiv.org/abs/1908.05612
Original
1908
Earlier work this paper cites.
H. Wei, L. Zhou, Y. Zhang, H. Li, R. Guo, and H. Wang, “Oriented objects as pairs of middle lines,” ISPRS Journal of Photogrammetry and Remote Sensing , 2019. [Online]. Available: http://arxiv.org/abs/1912.10694
Original
1912
Earlier work this paper cites.
R. A. Brooks and M. J. Mataric, “Real robots, real learning problems,” in Robot Learning , J. H. Connell and S. Mahadevan, Eds. Boston, MA: Springer US, 1993, pp. 193–213
1993
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J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding HD maps and agent dynamics from vectorized representation,” 2020. [Online]. Available: https://arxiv.org/abs/2005.04259
Original
2005
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” 2020. [Online]. Available: https://arxiv.org/abs/2006.11239
Original
2006
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” 2020. [Online]. Available: https://arxiv.org/abs/2010.02502
Original
2010
Earlier work this paper cites.
M. Fisher, D. Ritchie, M. Savva, T. Funkhouser, and P. Hanrahan, “Example-based synthesis of 3d object arrangements,” ACM Transactions on Graphics (TOG) , vol. 31, no. 6, pp. 1–11, 2012
2012
Earlier work this paper cites.
P. Sun, Y. Jiang, E. Xie, Z. Yuan, C. Wang, and P. Luo, “What makes for end-to-end object detection?” ICML , 2020. [Online]. Available: https://arxiv.org/abs/2012.05780
Original
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. 25, pp. 723–773, 2012. [Online]. Available: http://jmlr.org/papers/v13/gretton12a.html
2012
Earlier work this paper cites.

Similar

Then

S. Tan, K. Wong, S. Wang, S. Manivasagam, M. Ren, and R. Urtasun, “Scenegen: Learning to generate realistic traffic scenes,” CVPR , 2021. [Online]. Available: https://arxiv.org/abs/2101.06541
Original
2021
Later among the works it cites.
L. Bergamini, Y. Ye, O. Scheel, L. Chen, C. Hu, L. Del Pero, B. Osinski, H. Grimmett, and P. Ondruska, “SimNet: Learning Reactive Self-driving Simulations from Real-world Observations,” May 2021. [Online]. Available: http://arxiv.org/abs/2105.12332
Original
2021
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion Models Beat GANs on Image Synthesis,” June 2021. [Online]. Available: http://arxiv.org/abs/2105.05233
Original
2021
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” CVPR , 2021. [Online]. Available: https://arxiv.org/abs/2112.10752
Original
2021
Later among the works it cites.

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2023

Generating Driving Scenes with Diffusion

Pronovost, Ethan, Wang, Kai, Roy, Nick

Understand

In this paper we describe a learned method of traffic scene generation designed to simulate the output of the perception system of a self-driving car.

In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel combination of diffusion and object detection to directly create realistic and physically plausible arrangements of discrete bounding boxes for agents.
We show that our scene generation model is able to adapt to different regions in the US, producing scenarios that capture the intricacies of each region.

Built on

A. Kar, A. Prakash, M. Liu, E. Cameracci, J. Yuan, M. Rusiniak, D. Acuna, A. Torralba, and S. Fidler, “Meta-sim: Learning to generate synthetic datasets,” 2019. [Online]. Available: http://arxiv.org/abs/1904.11621
Original
1904
Earlier work this paper cites.
J. Hong, B. Sapp, and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,” CVPR , 2019. [Online]. Available: http://arxiv.org/abs/1906.08945
Original
1906
Earlier work this paper cites.
X. Yang, Q. Liu, J. Yan, and A. Li, “R3det: Refined single-stage detector with feature refinement for rotating object,” 2019. [Online]. Available: http://arxiv.org/abs/1908.05612
Original
1908
Earlier work this paper cites.
H. Wei, L. Zhou, Y. Zhang, H. Li, R. Guo, and H. Wang, “Oriented objects as pairs of middle lines,” ISPRS Journal of Photogrammetry and Remote Sensing , 2019. [Online]. Available: http://arxiv.org/abs/1912.10694
Original
1912
Earlier work this paper cites.
R. A. Brooks and M. J. Mataric, “Real robots, real learning problems,” in Robot Learning , J. H. Connell and S. Mahadevan, Eds. Boston, MA: Springer US, 1993, pp. 193–213
1993
Earlier work this paper cites.
J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding HD maps and agent dynamics from vectorized representation,” 2020. [Online]. Available: https://arxiv.org/abs/2005.04259
Original
2005
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” 2020. [Online]. Available: https://arxiv.org/abs/2006.11239
Original
2006
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” 2020. [Online]. Available: https://arxiv.org/abs/2010.02502
Original
2010
Earlier work this paper cites.
M. Fisher, D. Ritchie, M. Savva, T. Funkhouser, and P. Hanrahan, “Example-based synthesis of 3d object arrangements,” ACM Transactions on Graphics (TOG) , vol. 31, no. 6, pp. 1–11, 2012
2012
Earlier work this paper cites.
P. Sun, Y. Jiang, E. Xie, Z. Yuan, C. Wang, and P. Luo, “What makes for end-to-end object detection?” ICML , 2020. [Online]. Available: https://arxiv.org/abs/2012.05780
Original
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. 25, pp. 723–773, 2012. [Online]. Available: http://jmlr.org/papers/v13/gretton12a.html
2012
Earlier work this paper cites.

Similar

Then

S. Tan, K. Wong, S. Wang, S. Manivasagam, M. Ren, and R. Urtasun, “Scenegen: Learning to generate realistic traffic scenes,” CVPR , 2021. [Online]. Available: https://arxiv.org/abs/2101.06541
Original
2021
Later among the works it cites.
L. Bergamini, Y. Ye, O. Scheel, L. Chen, C. Hu, L. Del Pero, B. Osinski, H. Grimmett, and P. Ondruska, “SimNet: Learning Reactive Self-driving Simulations from Real-world Observations,” May 2021. [Online]. Available: http://arxiv.org/abs/2105.12332
Original
2021
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion Models Beat GANs on Image Synthesis,” June 2021. [Online]. Available: http://arxiv.org/abs/2105.05233
Original
2021
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” CVPR , 2021. [Online]. Available: https://arxiv.org/abs/2112.10752
Original
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2016

Cited alongside, same era.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework,” Nov. 2016. [Online]. Available: https://openreview.net/forum?id=Sy2fzU9gl

2016

Cited alongside, same era.

I. Loshchilov and F. Hutter, “Fixing weight decay regularization in adam,” ICLR , 2017. [Online]. Available: http://arxiv.org/abs/1711.05101

Original

2017

Cited alongside, same era.

A. Dosovitskiy, G. Ros, F. Codevilla, A. M. López, and V. Koltun, “CARLA: an open urban driving simulator,” 2017. [Online]. Available: http://arxiv.org/abs/1711.03938

Original

2017

Cited alongside, same era.

J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y. Zheng, and X. Xue, “Arbitrary-oriented scene text detection via rotation proposals,” 2017. [Online]. Available: http://arxiv.org/abs/1703.01086

Original

2017

Cited alongside, same era.

G. Izatt and R. Tedrake, “Generative modeling of environments with scene grammars and variational inference,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6891–6897

2020

Cited alongside, same era.

M. Zand, A. Etemad, and M. A. Greenspan, “Oriented bounding boxes for small and freely rotated objects,” IEEE Transactions on Geoscience and Remote Sensing , 2021. [Online]. Available: https://arxiv.org/abs/2104.11854

Original

2021

Later among the works it cites.

X. Yang, J. Yan, Q. Ming, W. Wang, X. Zhang, and Q. Tian, “Rethinking rotated object detection with gaussian wasserstein distance loss,” 2021. [Online]. Available: https://arxiv.org/abs/2101.11952

Original

2021

Later among the works it cites.

X. Yang, X. Yang, J. Yang, Q. Ming, W. Wang, Q. Tian, and J. Yan, “Learning high-precision bounding box for rotated object detection via kullback-leibler divergence,” 2021. [Online]. Available: https://arxiv.org/abs/2106.01883

Original