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Benchmark Organizers

Below you can find the organizers of this benchmark website listed in alphabetical ordering.

Lars Hammarstrand (Chalmers University of Technology) is an Assistant Professor at Chalmers University of Technology. He has previously worked at Volvo Car Group, where he was responsible for developing the sensor fusion platform for their active safety systems. His current research interests lie in the combination of Machine learning and Bayesian statistics in general and, in particular, with application to robust localization and perception for self-driving vehicles.

Fredrik Kahl (Chalmers University of Technology) is a Professor and head of the Computer Vision Group at Chalmers. He has co-organized several workshops including Computer Vision for Road Scene Understanding and Autonomous Driving and has given several tutorials at CVPR, ICCV and ECCV. He primary research interests include 3D computer vision, semantic scene understanding, optimization and deep learning theory.

Will Maddern (Nuro) leads the mapping and localisation efforts at Nuro (, a Silicon Valley startup building the first generation of self-driving on-road delivery robots to revolutionise local goods transport. Prior to joining Nuro, Will was a Senior Researcher with the Oxford Robotics Institute at the University of Oxford, and flagship lead for the Oxford RobotCar project ( He led a team focusing on autonomous driving in urban environments; primarily localisation, mapping and navigation using vision, LIDAR and radar, along with path planning, control, and obstacle perception using deep learning. Will is responsible for the Oxford RobotCar Dataset, the largest available dataset for vision and LIDAR-based long-term autonomy for on-road applications. He has co-organized a workshop on deep visual SLAM at CVPR 2018 and a tutorial on vision for autonomous driving at ICCV 2015.

Tomas Pajdla (Czech Technical University in Prague) is an Associate Professor at the CTU in Prague. He works in geometry and algebra of computer vision and robotics, 3D reconstruction, visual localization, place recognition and industrial vision. He contributed to introducing epipolar geometry of panoramic cameras, non-central camera models generated by linear mapping, generalized epipolar geometries, to developing solvers for minimal problems in structure from motion, solving image matching problem and to image-based localization. He co-authored works awarded prizes at OAGM 1998 and 2013, BMVC 2002 and ACCV 2014. He was a program chair/organizer of ECCV 2004, 2014, 3DV 2018, and numerous and tutorials CVPR. ICCV, ECCV workshops, e.g. CVVT 2010-2018, OMNIVIS 2007 and Minimal 2015.

Marc Pollefeys (ETH Zurich, Microsoft) is Director of Science leading a team of scientist and engineers to develop advanced perception capabilities for HoloLens. He is also a Professor of Computer Science at ETH Zurich and was elected Fellow of the IEEE in 2012. He is best known for his work in 3D computer vision, having been the first to develop a software pipeline to automatically turn photographs into 3D models, but also works on robotics, graphics and machine learning problems. Other noteworthy projects he worked on with collaborators at UNC Chapel Hill and ETH Zurich are real-time 3D scanning with mobile devices, a real-time pipeline for 3D reconstruction of cities from vehicle mounted-cameras, camera-based self-driving cars and the first fully autonomous vision-based drone. Most recently his academic research has focused on combining 3D reconstruction with semantic scene understanding. He has published over 300 peer-reviewed publications and holds several patents. His lab at ETH Zurich also developed the PixHawk auto-pilot which can be found in over half a million drones and he has co-founded several computer vision start-ups.

Torsten Sattler (Chalmers University of Technology) is an Associate Professor at Chalmers University of Technology. He has (co-)organized tutorials on visual localization at CVPR 2014, 2015, 2017 and at ECCV 2018. He also (co-)organized a workshop on localization and place recognition at CVPR 2015 and workshops on combining 3D reconstruction and semantic scene understanding at ICCV 2017 and ECCV 2018. He is working on semantic 3D reconstruction, semantic SLAM and semantic re-localization in order to allow robots and other mobile devices to autonomously navigate through challenging environments subject to illumination, seasonal, and geometric changes. Torsten has been an area chair for CVPR 2018, 3DV 2018, and GCPR 2018 and is an Associate Editor for ICRA 2019 and an Area Chair for 3DV 2019.

Josef Sivic (INRIA, Czech Technical University in Prague) holds a joint senior researcher position at Inria in Paris and Czech Technical University in Prague, where he leads a newly created team on Intelligent Machine Perception spanning both institutions. His papers have been awarded the Longuet-Higgins prize (CVPR’07) and the Helmholtz prize (ICCV’03 and ICCV’05) for fundamental contributions to computer vision that withstood the test of time. He has served as a program chair of ICCV’15.

Erik Stenborg is an industrial PhD student in the Signal processing research group and is involved in the project Vehicle Positioning, which aims to improve vehicle positioning to the point where it is reliable enough for autonomous driving.

Carl Toft (Chalmers University of Technology) is a PhD student in the computer vision group at Chalmers University of Technology. His main research interests lie in visual localization and mapping, and is currently working on developing localization methods that are robust even under large appearance variations in the environment due to weather, seasons and illumination changes.

Akihiko Torii (Tokyo Tech) is an Assistant Professor at Tokyo Institute of Technology. He has (co-)organized tutorials on visual localization at CVPR 2014, 2015 and 2017. He has been selected as an outstanding reviewer in ECCV 2012, ICCV 2015, CVPR 2016 and CVPR 2017. He contributed to providing several datasets (Pitts250k, Tokyo24/7, SFrevisited, InLoc) for outdoor/indoor place recognition and visual localization. He is working on feature and image matching, structure-from-motion, and camera re-localization for large-scale problems.