2 code implementations • 8 Jun 2023 • Faris Janjoš, Lars Rosenbaum, Maxim Dolgov, J. Marius Zöllner
The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables.
no code implementations • 26 Sep 2022 • Florian Drews, Di Feng, Florian Faion, Lars Rosenbaum, Michael Ulrich, Claudius Gläser
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection.
no code implementations • 18 Dec 2020 • Di Feng, Zining Wang, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer, Masayoshi Tomizuka, Wei Zhan
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
no code implementations • 10 Aug 2020 • Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving.
no code implementations • 7 Mar 2020 • Zining Wang, Di Feng, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer, Masayoshi Tomizuka, Wei Zhan
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
no code implementations • 1 Feb 2020 • Di Feng, Yifan Cao, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection.
no code implementations • 26 Sep 2019 • Di Feng, Lars Rosenbaum, Claudius Glaeser, Fabian Timm, Klaus Dietmayer
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving.
no code implementations • 16 Jul 2019 • Lukas Enderich, Fabian Timm, Lars Rosenbaum, Wolfram Burgard
Due to their high computational complexity, deep neural networks are still limited to powerful processing units.
1 code implementation • 21 Feb 2019 • Di Feng, Christian Haase-Schuetz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck, Klaus Dietmayer
This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving.
Robotics
no code implementations • 29 Jan 2019 • Di Feng, Xiao Wei, Lars Rosenbaum, Atsuto Maki, Klaus Dietmayer
Training a deep object detector for autonomous driving requires a huge amount of labeled data.
no code implementations • 14 Sep 2018 • Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
We validate our method on the KITTI object detection benchmark.
Robotics
no code implementations • 13 Apr 2018 • Di Feng, Lars Rosenbaum, Klaus Dietmayer
Experimental results show that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion.