Torsten Sattler
Associate Professor at
Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis
Torsten Sattler is an associate professor in the research group Computer vision and medical image analysis. His main research interests focus around developing robust and reliable 3D computer vision algorithms for applications such as Mixed Reality, Self-Driving Cars, and Robotics. To this end, Torsten works on integrating higher-level scene understanding into techniques such as visual localization and mapping. He is further interested in real-time computer vision algorithms and machine learning for computer vision tasks.
Source: chalmers.se
Showing 8 publications
2019
BAD SLAM: Bundle Adjusted Direct RGB-D SLAM
Thomas Schöps, Torsten Sattler, Marc Pollefeys
Paper in proceedings
2019
Understanding the Limitations of CNN-based Absolute Camera Pose Regression
Torsten Sattler, Qunjie Zhou, Marc Pollefeys et al
Paper in proceedings
2019
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
Thomas Schops, Torsten Sattler, Marc Pollefeys
IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. In Press
Journal article
2019
Night-to-day image translation for retrieval-based localization
Asha Anoosheh, Torsten Sattler, Radu Timofte et al
Proceedings - IEEE International Conference on Robotics and Automation. Vol. 2019-May, p. 5958-5964
Paper in proceedings
2019
Hybrid scene Compression for Visual Localization
Federico Camposeco, Andrea Cohen, Marc Pollefeys et al
Paper in proceedings
2019
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
Måns Larsson, Erik Stenborg, Lars Hammarstrand et al
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Paper in proceedings
2019
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla et al
Paper in proceedings
2019
Incremental visual-inertial 3d mesh generation with structural regularities
Antoni Rosinol, Torsten Sattler, Marc Pollefeys et al
Proceedings - IEEE International Conference on Robotics and Automation. Vol. 2019-May, p. 8220-8226
Paper in proceedings
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