Our Research
We investigate robots that can understand their environment semantically and geometrically, in order to perform manipulation and other safety critical tasks in proximity to humans. This encompasses semantic understanding under open-set conditions, map representations of the environment, active perception and planning, as well as adaptation and continual self-supervised learning.Open-Vocabulary and Semantic-Aware Reasoning for Search and Retrieval of Objects in Dynamic and Concealed Spaces
R Menon, Y Schmiede, M Bennewitz, H Blum
IROS 2025
SNI-SLAM++: Tightly-Coupled Semantic Neural Implicit SLAM
S Zhu, G Wang, H Blum, Z Wang, G Zhang, D Cremers, M Pollefeys, H Wang
T-PAMI 2025
doiwebsitecodeieeexplore.ieee.org3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection
Y Yang, L Piccinelli, M Segu, S Li, R Huang, Y Fu, M Pollefeys, H Blum, Z Bauer
ICCV 2025
arxivPDFwebsitecodeopenaccess.thecvf.comCroCoDL: Cross-device Collaborative Dataset for Localization
H Blum, A Mercurio, J O’Reilly, T Engelbracht, M Dusmanu, M Pollefeys, Z Bauer
CVPR 2025
doilocalizoo.comARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
G Ji, S Weder, F Engelmann, M Pollefeys, H Blum
CVPR 2025
arxivPDFlabelmaker.orgcodeopenaccess.thecvf.comSNI-SLAM: Semantic Neural Implicit SLAM
S Zhu, G Wang, H Blum, J Liu, L Song, M Pollefeys, H Wang
CVPR 2024
arxivPDFcodeopenaccess.thecvf.comLabelMaker: Automatic Semantic Label Generation from RGB-D Trajectories
S Weder, H Blum, F Engelmann, M Pollefeys
3DV 2024
arxivPDFlabelmaker.orgcodeieeexplore.ieee.orgRobot Perception and Learning Lab
