Search Results for author: Lars Rosenbaum

Found 12 papers, 2 papers with code

Unscented Autoencoder

2 code implementations8 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.

DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars

no code implementations26 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.

3D Object Detection Depth Estimation +1

Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection

no code implementations18 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.

Autonomous Driving Object +2

Inferring Spatial Uncertainty in Object Detection

no code implementations7 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.

Autonomous Driving Object +2

Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

no code implementations1 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.

3D Object Detection Autonomous Driving +2

Learning Multimodal Fixed-Point Weights using Gradient Descent

no code implementations16 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.

Quantization

Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

1 code implementation21 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

Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

no code implementations13 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.

Autonomous Driving General Classification +3

Cannot find the paper you are looking for? You can Submit a new open access paper.