July 27th, 2021 
Daniela Lopez de Luise The workshop from the 3D-DLAD-v3 (3rd 3D Deep Learning for Autonomous Driving) workshop organized as a part of the flagship automotive conference Intelligent Vehicles 2021 https://2021.ieee-iv.org/ are now available online here :
A recall of the talks presented at the workshop :
- On Monocular Depth Estimation: (1) MonoDEVS ; (2) Multi-modal Co-training, Dr. Antonio M. López
- Point-based recognition, Prof. Philipp Krähenbühl
- Lidar Segmentation at Motional, Venice Liong
- Co-Development of Automatic Annotation for Machine Learning and Sensor Fusion Improvement System, Stefan Haag
- Radar Perception for Automated Driving – Data and Methods, Ole Schumann
- Perception Data Pipeline at Innoviz Technologies, Amir Day
- [paper 1] The Oxford Road Boundaries Dataset, Tarlan Suleymanov*, Matthew Gadd, Daniele De Martini, Paul Newman
- [paper2] Unsupervised Joint Multi-Task Learning of Vision Geometry Tasks, Prabhash Kumar Jha*, Doychin Tsanev, Luka Lukic
- [paper3] CFTrack: Center-based Radar and Camera Fusion for 3D Multi Object Tracking, Ramin Nabati, Landon Harris, Hairong Qi
- [paper4] Machine learning based 3D object detection for navigation in unstructured environments, Gjorgji Nikolovski, Michael Reke*, Ingo Elsen, Stefan Schiffer
- [paper5] Pruning CNNs for LiDAR-based Perception in Resource Constrained Environments, Manoj Vemparala*, Anmol Singh, Ahmed Mzid, Nael Fasfous, Alexander Frickenstein, Florian Mirus, Hans Joerg Voegel, Naveen Shankar Nagaraja, Walter Stechele
- Modern methods of visual localization, Dr. Martin Humenberger
- All-In-One Drive: A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds, Xinshuo Weng
- Offboard Perception for Autonomous Driving, Charles R Qi
- Using Artificial Intelligence layer to transform high-resolution radar point cloud into insights for Autonomous Driving applications, Sani Ronen
- Self-supervised 3D vision, Dr. Rareș Ambruș
Best regards,
DLAD Team
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