3D-DLAD-v3 (third 3D Deep Learning for Autonomous Driving) workshop is the 6th workshop organized as part of DLAD workshop series. It is organized as a part of the flagship automotive conference Intelligent Vehicles https://2021.ieee-iv.org/.

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 in 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.

We are soliciting contributions in deep learning on 3D data applied to autonomous driving in (but not limited to) the following topics. Please feel free to contact us if there are any questions.

TOPICS : 

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

Deep Learning for Radar pointclouds

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).

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

Workshop link : https://sites.google.com/view/3d-dlad-v3-iv2021/home

Submission instructions : https://2021.ieee-iv.org/information-for-authors/

Location : Nagoya, Japan

Submission : 15th March 2021 (firm deadline, no extension)

Acceptance Notification : 25th April 2021

Workshop Date : 11th July 2021

Contact: ravi.kiran@navya.tech and senthil.yogamani@valeo.com

Workshop Organizers:

B Ravi Kiran, Navya, France

Senthil Yogamani, Valeo Vision Systems, Ireland

Victor Vaquero, Research Engineer, IVEX.ai

Patrick Perez, Valeo.AI, France

Bharanidhar Duraisamy, Daimler, Germany

Dan Levi, GM, Israel

Abhinav Valada, University of Freiburg, Germany

Lars Kunze, Oxford University, UK

Markus Enzweiler, Daimler, Germany

Ahmad El Sallab, Valeo AI Research, Egypt

Sumanth Chennupati, Wyze Labs, USA

Stefan Milz, Spleenlab.ai , Germany

Hazem Rashed, Valeo AI Research, Egypt

Jean-Emmanuel Deschaud, MINES ParisTech, France