Built on

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” Proceedings of the 2nd International Conference on Learning Representations. , 2013
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434 , 2015
Original
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 , 2015
Original
2015
Earlier work this paper cites.
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al. , “Shapenet: An information-rich 3D model repository,” arXiv preprint arXiv:1512.03012 , 2015
Original
2015
Earlier work this paper cites.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” arXiv preprint arXiv:1512.09300 , 2015
Original
2015
Earlier work this paper cites.
B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3D lidar using fully convolutional network,” arXiv preprint arXiv:1608.07916 , 2016
Original
2016
Earlier work this paper cites.
P. Ondruska, J. Dequaire, D. Z. Wang, and I. Posner, “End-to-end tracking and semantic segmentation using recurrent neural networks,” arXiv preprint arXiv:1604.05091 , 2016
Original
2016
Earlier work this paper cites.
A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” arXiv preprint arXiv:1601.06759 , 2016
Original
2016
Earlier work this paper cites.
C. Doersch, “Tutorial on variational autoencoders,” arXiv preprint arXiv:1606.05908 , 2016
Original
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems , 2016, pp. 2234–2242
2016
Earlier work this paper cites.

Similar

D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved variational inference with inverse autoregressive flow,” in Advances in neural information processing systems , 2016, pp. 4743–4751
2016
Cited alongside, same era.
W. Lotter, G. Kreiman, and D. Cox, “Deep predictive coding networks for video prediction and unsupervised learning,” arXiv preprint arXiv:1605.08104 , 2016
Original
2016
Cited alongside, same era.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv preprint arXiv:1609.08144 , 2016
Original
2016
Cited alongside, same era.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3D object detection,” arXiv preprint arXiv:1711.06396 , 2017
Original
2017
Cited alongside, same era.

Then

T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196 , 2017
Original
2017
Later among the works it cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” arXiv preprint arXiv:1711.03938 , 2017
Original
2017
Later among the works it cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems , 2017, pp. 6626–6637
2017
Later among the works it cites.
M. Velas, M. Spanel, M. Hradis, and A. Herout, “Cnn for very fast ground segmentation in velodyne lidar data,” in Autonomous Robot Systems and Competitions (ICARSC), 2018 IEEE International Conference on . IEEE, 2018, pp. 97–103
2018
Closest in time.

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2018

Deep Generative Modeling of LiDAR Data

Caccia, Lucas, van Hoof, Herke, Courville, Aaron et al.

Understand

Building models capable of generating structured output is a key challenge for AI and robotics.

While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization.
In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.
Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data.

Built on

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” Proceedings of the 2nd International Conference on Learning Representations. , 2013
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434 , 2015
Original
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 , 2015
Original
2015
Earlier work this paper cites.
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al. , “Shapenet: An information-rich 3D model repository,” arXiv preprint arXiv:1512.03012 , 2015
Original
2015
Earlier work this paper cites.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” arXiv preprint arXiv:1512.09300 , 2015
Original
2015
Earlier work this paper cites.
B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3D lidar using fully convolutional network,” arXiv preprint arXiv:1608.07916 , 2016
Original
2016
Earlier work this paper cites.
P. Ondruska, J. Dequaire, D. Z. Wang, and I. Posner, “End-to-end tracking and semantic segmentation using recurrent neural networks,” arXiv preprint arXiv:1604.05091 , 2016
Original
2016
Earlier work this paper cites.
A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” arXiv preprint arXiv:1601.06759 , 2016
Original
2016
Earlier work this paper cites.
C. Doersch, “Tutorial on variational autoencoders,” arXiv preprint arXiv:1606.05908 , 2016
Original
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems , 2016, pp. 2234–2242
2016
Earlier work this paper cites.

Similar

D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved variational inference with inverse autoregressive flow,” in Advances in neural information processing systems , 2016, pp. 4743–4751
2016
Cited alongside, same era.
W. Lotter, G. Kreiman, and D. Cox, “Deep predictive coding networks for video prediction and unsupervised learning,” arXiv preprint arXiv:1605.08104 , 2016
Original
2016
Cited alongside, same era.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv preprint arXiv:1609.08144 , 2016
Original
2016
Cited alongside, same era.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3D object detection,” arXiv preprint arXiv:1711.06396 , 2017
Original
2017
Cited alongside, same era.

Then

T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196 , 2017
Original
2017
Later among the works it cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” arXiv preprint arXiv:1711.03938 , 2017
Original
2017
Later among the works it cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in Neural Information Processing Systems , 2017, pp. 6626–6637
2017
Later among the works it cites.
M. Velas, M. Spanel, M. Hradis, and A. Herout, “Cnn for very fast ground segmentation in velodyne lidar data,” in Autonomous Robot Systems and Competitions (ICARSC), 2018 IEEE International Conference on . IEEE, 2018, pp. 97–103
2018
Closest in time.

Beyond the bibliography

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

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alphaXiv is searching for related work…

M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner, “Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 1355–1361

2017

Cited alongside, same era.

A. Dewan, G. L. Oliveira, and W. Burgard, “Deep semantic classification for 3D lidar data,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 3544–3549

2017

Cited alongside, same era.

L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using fully convolutional neural networks,” arXiv preprint arXiv:1703.03613 , 2017

Original

2017

Cited alongside, same era.

B. Li, “3D fully convolutional network for vehicle detection in point cloud,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 1513–1518

2017

Cited alongside, same era.

V. Vaquero, I. del Pino, F. Moreno-Noguer, J. Solà, A. Sanfeliu, and J. Andrade-Cetto, “Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios,” in Mobile Robots (ECMR), 2017 European Conference on . IEEE, 2017, pp. 1–7

2017

Cited alongside, same era.

P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Representation learning and adversarial generation of 3D point clouds,” arXiv preprint arXiv:1707.02392 , 2017

Original

2017

Cited alongside, same era.

H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3D object reconstruction from a single image.” in CVPR , vol. 2, no. 4, 2017, p. 6

2017

Cited alongside, same era.

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3D classification and segmentation,” Proc. Computer Vision and Pattern Recognition (CVPR), IEEE , vol. 1, no. 2, p. 4, 2017

2017

Cited alongside, same era.

V. Vaquero, A. Sanfeliu, and F. Moreno-Noguer, “Deep lidar cnn to understand the dynamics of moving vehicles,” arXiv preprint arXiv:1808.09526 , 2018

Original

2018

Closest in time.

P. Tomasello, S. Sidhu, A. Shen, M. W. Moskewicz, N. Redmon, G. Joshi, R. Phadte, P. Jain, and F. Iandola, “Dscnet: Replicating lidar point clouds with deep sensor cloning,” arXiv preprint arXiv:1811.07070 , 2018

Original

2018

Closest in time.

T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry, “Atlasnet: A papier-mâché approach to learning 3D surface generation,” arXiv preprint arXiv:1802.05384 , 2018

Original

2018

Closest in time.

Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , vol. 3, 2018

2018

Closest in time.

R. Liu, J. Lehman, P. Molino, F. P. Such, E. Frank, A. Sergeev, and J. Yosinski, “An intriguing failing of convolutional neural networks and the coordconv solution,” in Advances in Neural Information Processing Systems , 2018, pp. 9628–9639

2018

Closest in time.

S. Kolouri, G. K. Rohde, and H. Hoffmann, “Sliced wasserstein distance for learning gaussian mixture models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3427–3436

2018

Closest in time.

A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard gan,” arXiv preprint arXiv:1807.00734 , 2018

Original

2018

Closest in time.

H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” arXiv preprint arXiv:1805.08318 , 2018

Original

2018

Closest in time.