[visionlist] [CfP] IV 2021 : 3D-DLAD-v3 third workshop on 3D Deep Learning for Autonomous Driving at Intelligent Vehicules 2021

[Ravi Kiran]

We apologise in advance for multiple reposts/copies.

*CALL FOR PAPERS 3D-DLAD-v3 2021*

*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 at navya.tech and senthil.yogamani at 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
Kuo-Chin Lien, Appen USA
Naveen Shankar Nagaraja, BMW Group, Munich
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://visionscience.com/pipermail/visionlist_visionscience.com/attachments/20210215/288e28c7/attachment.html>