30 citations · 39 across the 5 of their papers we have counts for
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3 papers · 1 filter
cs.RO2020
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
Di Feng, Yifan Cao, Lars Rosenbaum +2
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertai…
cs.RO2019★ 30 cited
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
Di Feng, Lars Rosenbaum, Claudius Glaeser +2
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving. Recently, many methods have been proposed to model uncertainties in deep learning base…
cs.RO2018
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
Di Feng, Lars Rosenbaum, Fabian Timm +1
We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss funct…