The 3D-DLAD (3D Deep Learning for Autonomous Driving) workshop is organized as part of the flagship automotive conference, Intelligent Vehicles http://iv2019.org.

The previous edition of this workshop, Deep learning for autonomous driving (DLAD) was held at ITSC 2017, Japan.

The presentations/talks from previous edition of the workshop can be found here https://sites.google.com/site/dlitsc17/program

For this edition, we are soliciting contributions in the domain of deep learning for 3D data applied to autonomous driving in (but not limited to) the following 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

Workshop : https://sites.google.com/view/3d-dlad-iv2019/ Location : Paris, France

Submission : 7th Feb 2019 (submission portal is not open yet)

Acceptance Notification : 29th March 2019 Workshop Date : 9th June 2019

Contact: [email protected], [email protected] and [email protected]

Please feel free to contact us if there are any questions. We are sorry if this is a repost.

Abstract: Deep Learning has become a de-facto tool in Computer Vision and 3D processing with boosted performance and accuracy for diverse tasks such as object classification, detection, optical flow estimation, motion segmentation, mapping, etc. Lidar sensors are playing an important role in the development of Autonomous Vehicles as they overcome some of the many drawbacks of a camera based system, such as degraded performance under changes in illumination and weather conditions. In addition, Lidar sensors capture a wider field of view, and directly obtain 3D information. This is essential to assure the security of the different agents and obstacles in the scene. It is a computationally challenging task to process more than 100k points per scan in realtime within modern perception pipelines. Following the said motivations, finally to address the growing interest on deep representation learning for lidar point-clouds, in both academic as well as industrial research domains for autonomous driving, we invite submissions to the current workshop to disseminate the latest research.