2024
Xue, Y., Li, H., Leutenegger, S., Stueckler, J.
Event-based Non-Rigid Reconstruction of Low-Rank Parametrized Deformations from Contours
International Journal of Computer Vision (IJCV), 2024 (article)
2023
Elich, C., Kirchdorfer, L., Köhler, J. M., Schott, L.
Challenging Common Assumptions in Multi-task Learning
abs/2311.04698, CoRR/arxiv, 2023 (techreport)
2022
Elich, C., Oswald, M. R., Pollefeys, M., Stueckler, J.
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors
Computer Vision and Image Understanding (CVIU), 2022 (article) Accepted
Li, H., Stueckler, J.
Observability Analysis of Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models
abs/2204.06651, CoRR/arxiv, 2022 (techreport)
Li, H., Stueckler, J.
Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models
IEEE Robotics and Automation Letters (RA-L), 2022, Accepted for oral presentation at IEEE ICRA 2023 (article) Accepted
2021
Kandukuri, R., Achterhold, J., Moeller, M., Stueckler, J.
Physical Representation Learning and Parameter Identification from Video Using Differentiable Physics
International Journal of Computer Vision, 2021 (article)
2020
Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J.
Numerical Quadrature for Probabilistic Policy Search
IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(1):164-175, 2020 (article)
Usenko, V., Stumberg, L. V., Stückler, J., Cremers, D.
TUM Flyers: Vision-Based MAV Navigation for Systematic Inspection of Structures
In Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users: The Experience of the European Robotics Challenges, 136, pages: 189-209, Springer International Publishing, 2020 (inbook)
Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D.
Visual-Inertial Mapping with Non-Linear Factor Recovery
IEEE Robotics and Automation Letters (RA-L), 5, 2020, presented at IEEE International Conference on Robotics and Automation (ICRA) 2020, preprint arXiv:1904.06504 (article)
2018
Ma, L., Stueckler, J., Wu, T., Cremers, D.
Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform
arxiv, 2018, arXiv:1808.01834 (techreport)