30 citations · 43 across the 5 of their papers we have counts for
7 papers
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
Di Feng, Lars Rosenbaum, Fabian Timm +1
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive prob…
Inferring Spatial Uncertainty in Object Detection
Zining Wang, Di Feng, Yiyang Zhou +5
The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-pro…
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…
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…
Learning Multimodal Fixed-Point Weights using Gradient Descent
Lukas Enderich, Fabian Timm, Lars Rosenbaum +1
Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-poin…
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Di Feng, Christian Haase-Schütz, Lars Rosenbaum +5
Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually e…