3D-DLAD 2019

Workshop on 3D Deep Learning for Automated Driving

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 this year it is the 30th anniversary of the conference this year. This workshop is a follow up of last year’s ITSC workshop on ‘Deep Learning for Autonomous Driving’ (link) but focused on 3D data processing from Lidar, cameras and TOF sensors.

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

References:

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