3D-DLAD 2022

Intelligent Transportation Systems (ITS) is the IEEE society for automotive broadly including autonomous driving. The Intelligent Vehicles Symposium (IV’2022) (link) is a premier forum sponsored by the IEEE Intelligent Transportation Systems Society (ITSS) and it is the 32nd edition of the conference this year. This workshop is the fourth edition of our ITS workshop series on ‘Deep Learning for Autonomous Driving’ (DLAD) 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. 

Previous edition of the workshop series: 

• Workshop on Deep Learning for Autonomous Driving (DLAD) [website] - ITSC 2017, Yokohama

• [v1] 3D-Deep Learning for Automated Driving (3D-DLAD) [website] - IV 2019, Paris

• Workshop Deep learning for Automated Driving : Beyond Perception [site] (DLAD-BP) - ITSC 2019, Auckland

• Collaborative Perception & Federated ML for Autonomous Driving [site](CoFED-DLAD 2020)

• [v2] 3D-Deep Learning for Automated Driving (3D-DLAD) [website] - IV 2020, Las Vegas, USA

• [v3] 3D-Deep Learning for Automated Driving (3D-DLAD) [website] - IV 2021, Nagoya, Japan 2021

List of topics:

• Deep Learning for Lidar based clustering, road extraction object detection and/or tracking.

• Deep Learning for Radar pointclouds

• Deep Learning for Camera based Monocular 3D-Detection & Depth Estimation

• Deep Learning for TOF sensor-based driver monitoring

• New lidar based technologies and sensors.

• Deep Learning for Lidar localization, VSLAM, meshing, pointcloud inpainting

• Deep Learning for Odometry and Map/HDmaps generation with Lidar cues.

• Deep fusion of automotive sensors (Lidar, Camera, Radar).

• Graph Neural Networks (GNNs) for 3D data, meshes and pointclouds

• Design of datasets and active learning methods for pointclouds

• Synthetic Lidar sensors & Simulation-to-real 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).

• 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

References:

1. Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds, Simon, Martin, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019.

2. Scalability in perception for autonomous driving: Waymo open dataset, Sun, Pei, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

3. Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection, Bewley, Alex, et al. Waymo, arXiv preprint arXiv:2005.09927 (2020).

4. What you see is what you get: Exploiting visibility for 3d object detection, Hu, Peiyun, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

5. 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).

6. 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).

7. 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).

8. 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).

9. 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).

10. 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).

11. 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).

12. Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network. Roynard, X., Deschaud, J. E., & Goulette, F.

13. FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds. arXiv preprint arXiv:1712.07262. Yang, Y., Feng, C., Shen, Y., & Tian, D. (2017).

14. 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).

15. 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). 

16. 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). 

17. Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019). SemanticKITTI: A dataset for semantic scene understanding of lidar sequences. In Proceedings of the IEEE International Conference on Computer Vision (pp. 9297-9307).  [link]

18. Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., & Zhao, H. (2020). SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances. arXiv preprint arXiv:2002.09147.  [link]

19. Geyer, Jakob, et al. "A2D2: AEV autonomous driving dataset." (2019).  [link]

20. 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).

21. 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).

22. 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).

23. Caesar, Holger, et al. "nuscenes: A multimodal dataset for autonomous driving." arXiv preprint arXiv:1903.11027 (2019).  [link]

24. Pham, Quang-Hieu, et al. "A* 3D Dataset: Towards Autonomous Driving in Challenging Environments." arXiv preprint arXiv:1909.07541 (2019).  [link]

25. Aksoy, Eren Erdal, Saimir Baci, and Selcuk Cavdar. "SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving." arXiv preprint arXiv:1909.08291 (2019).  [link]

26. ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. arXiv preprint arXiv:1808.03506. Lyu, Y., Bai, L., & Huang, X. (2018).

27. Mei, Jilin, and Huijing Zhao. "Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene." arXiv preprint arXiv:2003.13926 (2020).  [link]

28. 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).

29. 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.

30. 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. 

31. DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds Ding L, Feng C. arXiv preprint arXiv:1811.11397 (2018).

32. The perfect match: 3d point cloud matching with smoothed densities, Gojcic, Zan, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.