3D-DLAD 2019

3D-Deep Learning for Automated Driving

2nd Edition of Deep Learning for Automated Driving (DLAD) workshop

IEEE Intelligent Vehicles Symposium (IV’19) - Paris, France, June 9-12, 2019.

Intelligent Transportation Systems (ITS) is the IEEE society for automotive broadly including autonomous driving. The Intelligent Vehicles Symposium (IV’19) (link) is a premier forum sponsored by the IEEE Intelligent Transportation Systems Society (ITSS) and it is the 30th anniversary of the conference this year. This workshop is the second edition of our ITSC workshop on ‘Deep Learning for Autonomous Driving’ (DLAD) (link) but focused on 3D data processing from Lidar, cameras and TOF sensors. Hence it is called 3D-DLAD workshop.

Deep Learning has become a de-facto tool in Computer Vision and 3D processing by boosting performance and accuracy for diverse tasks such as object classification, detection, optical flow estimation, motion segmentation, mapping, etc. Recently Lidar sensors are playing an important role in the development of Autonomous Vehicles as they overcome some of the main drawbacks of a camera like degraded performance under changes in illumination and weather conditions. In addition, Lidar sensors are capturing a wider field of view while directly obtaining 3D information, which is essential to assure the security of the different traffic players. However, it becomes a computationally challenging task to process daunting magnitudes of more than 100k points per scan. To address the growing interest on deep learning for lidar point-clouds, both from an academic research and industry in the domain of autonomous driving, we propose the current workshop to disseminate the latest research.

List of topics:

  • Deep Learning for Lidar based object detection and/or tracking.
  • Deep Learning for Lidar point-cloud clustering and road segmentation.
  • Deep Learning for computer vision point-cloud processing (VSLAM, meshing, inpainting)
  • Deep Learning for TOF sensor based driver monitoring
  • Deep Learning for Odometry and Map/HDmaps generation with Lidar cues.
  • Deep fusion of automotive sensors (Lidar, Camera, Radar).
  • Design of datasets (Synthetic Lidar sensors & Transfer learning).
  • Cross-modal feature extraction for Sparse output sensors like Lidar.
  • Generalization techniques for different Lidar sensors, multi-Lidar setup and point densities.
  • Lidar based maps, HDmaps, prior maps, occupancy grids
  • Real-time implementation on embedded platforms (Efficient design & hardware accelerators).
  • Challenges of deployment in a commercial system (Functional safety & High accuracy).
  • New lidar based technologies and sensors.
  • End to end learning of driving with Lidar information (Single model & modular end-to-end)
  • Deep learning for dense Lidar point cloud generation from sparse Lidars and other modalities


  1. Complex-YOLO: Real-time 3D Object Detection on Point Clouds. arXiv preprint arXiv:1803.06199. Simon, M., Milz, S., Amende, K., & Gross, H. M. (2018).
  2. PIXOR: Real-Time 3D Object Detection From Point Clouds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7652-7660). Yang, B., Luo, W., & Urtasun, R. (2018).
  3. Multi-view 3d object detection network for autonomous driving. In IEEE CVPR (Vol. 1, No. 2, p. 3). Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017, July).
  4. Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks. In Robotics and Automation (ICRA), 2017 IEEE International Conference on (pp. 1355-1361). IEEE. Engelcke, M., Rao, D., Wang, D. Z., Tong, C. H., & Posner, I. (2017, May).
  5. Detection and tracking of moving objects using 2.5 d motion grids. In Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on (pp. 788-793). IEEE. Asvadi, A., Peixoto, P., & Nunes, U. (2015, September).
  6. DepthCN: Vehicle detection using 3D-LIDAR and ConvNet. In Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on (pp. 1-6). IEEE. Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., & Nunes, U. J. (2017, October).
  7. Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1887-1893). IEEE. Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018, May).
  8. Efficient online segmentation for sparse 3d laser scans. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 85(1), 41-52. Bogoslavskyi, I., & Stachniss, C. (2017).
  9. Pointnet: Deep learning on point sets for 3d classification and segmentation. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 1(2), 4. Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017).
  10. Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network. Roynard, X., Deschaud, J. E., & Goulette, F.
  11. FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds. arXiv preprint arXiv:1712.07262. Yang, Y., Feng, C., Shen, Y., & Tian, D. (2017).
  12. Urban 3D segmentation and modelling from street view images and LiDAR point clouds. Machine Vision and Applications, 28(7), 679-694. Babahajiani, P., Fan, L., Kämäräinen, J. K., & Gabbouj, M. (2017).
  13. HDNET: Exploiting HD Maps for 3D Object Detection. Proceedings of The 2nd Conference on Robot Learning, in PMLR 87:146-155 Yang, B., Liang, M. & Urtasun, R.. (2018).
  14. Fast LIDAR localization using multiresolution Gaussian mixture maps. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 2814-2821). IEEE. Wolcott, R. W., & Eustice, R. M. (2015, May).
  15. ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. arXiv preprint arXiv:1808.03506. Lyu, Y., Bai, L., & Huang, X. (2018).
  16. Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 2027-2030). Roynard, X., Deschaud, J. E., & Goulette, F. (2018).
  17. A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (pp. 458-464). ACM. Yue, X., Wu, B., Seshia, S. A., Keutzer, K., & Sangiovanni-Vincentelli, A. L. (2018, June).
  18. 1 year, 1000 km: The Oxford RobotCar dataset. The International Journal of Robotics Research, 36(1), 3-15. Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017).
  19. nuScenes : public large-scale dataset for autonomous driving
  20. PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds. arXiv preprint arXiv:1806.02170. Behl, A., Paschalidou, D., Donné, S., & Geiger, A. (2018).
  21. Learning to Localize Using a LiDAR Intensity Map Ioan Andrei Barsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun ; Proceedings of The 2nd Conference on Robot Learning, PMLR 87:605-616, 2018.
  22. 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration, Yew, Zi Jian, and Gim Hee Lee, European Conference on Computer Vision (ECCV). Vol. 1. No. 2. 2018.
  23. DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds Ding L, Feng C. arXiv preprint arXiv:1811.11397 (2018).
  24. The Perfect Match: 3D Point Cloud Matching with Smoothed Densities Gojcic, Z., Zhou, C., Wegner, J. D., & Wieser, A. arXiv preprint arXiv:1811.06879 (2018).

Related workshops :

  • Workshop on Deep Learning for Autonomous Driving (DLAD 2017) [site], ITSC 2017
  • Workshop Deep learning for Automated Driving : Beyond Perception [site], ITSC 2019