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Learning Common and Transferable Feature Representations for Multi-Modal Data
(2020)2020 IEEE Intelligent Vehicles Symposium (IV)LiDAR sensors are crucial in automotive perception for accurate object detection. However, LiDAR data is hard to interpret for humans and consequently time-consuming to label. Whereas camera data is easy interpretable and thus, comparably simpler to label. Within this work we present a transductive transfer learning approach to transfer the knowledge for the object detection task from images to point cloud data. We propose a multi-modal ...Conference Paper -
Object Classification Based on Unsupervised Learned Multi-Modal Features For Overcoming Sensor Failures
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
3D Ground Point Classification for Automotive Scenarios
(2018)2018 21st International Conference on Intelligent Transportation Systems (ITSC)Autonomous driving applications must be provided with information about other road users and road side infrastructure by object detection modules. These modules often process point clouds sensed by light detection and ranging (LiDAR) sensors. Within the captured point cloud a large amount of points correspond to physical locations on the ground. These points do not hold information about road users, obstacles or road side infrastructure. ...Conference Paper