Niklas Hanselmann

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Andreas GeigerProfessor of Computer Science, University of Tübingen and Tübingen AI CenterVerified email at uni-tuebingen.de
Katrin RenzUniversity of Tübingen, Tübingen AI Center & IMPRS-ISVerified email at uni-tuebingen.de
Apratim BhattacharyyaQualcomm AI ResearchVerified email at qti.qualcomm.com
Kashyap ChittaUniversity of TübingenVerified email at uni-tuebingen.de
Marius CordtsMercedes-Benz AG R&D - Automated DrivingVerified email at mercedes-benz.com
Shuxiao DingMercedes-Benz AG & University of BonnVerified email at mercedes-benz.com
Xieyuanli ChenAssociate Professor at NUDT ChinaVerified email at nudt.edu.cn
Peizheng LiMercedes-Benz AG R&D & University of TuebingenVerified email at mercedes-benz.com
Juergen GallUniversity of BonnVerified email at iai.uni-bonn.de
Simon DollUniversity of TübingenVerified email at student.uni-tuebingen.de
Lukas SchneiderVerified email at schneiderdt.de
Hendrik P. A. LenschProfessor of Computer Graphics, University of TübingenVerified email at uni-tuebingen.de
Markus EnzweilerProfessor of Computer Science, Esslingen University of Applied SciencesVerified email at hs-esslingen.de
Nick SchneiderTorc.AIVerified email at nick-schneider.me
Federico TombariGoogle, TU MunichVerified email at in.tum.de
Andong TanHong Kong University of Science and TechnologyVerified email at tum.de

Niklas Hanselmann

Mercedes-Benz AG R&D & University of Tuebingen

Verified email at mercedes-benz.com - Homepage

Computer Vision Deep Learning Generative Models Representation Learning


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King: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients N Hanselmann, K Renz, K Chitta, A Bhattacharyya, A Geiger European Conference on Computer Vision (ECCV), 335–352, 2022	78	2022
Visibility guided nms: Efficient boosting of amodal object detection in crowded traffic scenes N Gählert, N Hanselmann, U Franke, J Denzler NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving, 2020	27	2020
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View P Li, S Ding, X Chen, N Hanselmann, M Cordts, J Gall International Joint Conference on Artificial Intelligence (IJCAI), 1080-1088, 2023	17	2023
STAR-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations S Doll, N Hanselmann, L Schneider, R Schulz, M Enzweiler, HPA Lensch IEEE Robotics and Automation Letters (RA-L), 2023	4	2023
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation N Hanselmann, N Schneider, B Ortelt, A Geiger IEEE Intelligent Vehicles Symposium (IV), 532-539, 2021	3	2021
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving S Doll, N Hanselmann, L Schneider, R Schulz, M Cordts, M Enzweiler, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2024		2024
Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features A Tan, N Hanselmann, S Ding, F Tombari, M Cordts ECCV 2022 Workshop on Learning from Limited and Imperfect Data (L2ID), 118-133, 2022		2022
Supplementary Material for KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients N Hanselmann, K Renz, K Chitta, A Bhattacharyya, A Geiger
Supplementary For: Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation N Hanselmann, N Schneider, B Ortelt, A Geiger

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