The Visual Localization Benchmark

by Torsten Sattler1 Will Maddern2 Carl Toft3 Akihiko Torii4 Lars Hammarstrand3 Erik Stenborg3 Daniel Safari4,5 Masatoshi Okutomi4 Marc Pollefeys1,6 Josef Sivic7,8 Fredrik Kahl3,9 Tomas Pajdla8CC-BY-NC-SA

The Visual Localization Benchmark

Visual localization is the problem of estimating the 6 Degree-of-Freedom (DoF) camera pose from which a given image was taken relative to a reference scene representation. Visual localization is a key technology for applications such as Augmented, Mixed, and Virtual Reality, as well as for robotics, e.g., for self-driving cars. In order to evaluate visual localization over longer periods of time, we provide benchmark datasets aimed at evaluating 6 DoF pose estimation accuracy over large appearance variations caused by changes in seasonal (summer, winter, spring, etc.) and illumination (dawn, day, sunset, night) conditions. Each dataset consists of a set of reference images, together with their corresponding ground truth poses, and a set of query images. A triangulated 3D model is provided for each dataset and can be used by structure-based localization approaches. To ensure fairness and comparability of results, the reference poses for the query images is withheld and we provide an evaluation service to measure pose accuracy.

Dataset Attributes

Label SVG
TasksObject Localizatoin
Label SVG
CategoriesTracking, Localization, 3D, Vehicle
Label SVG
SensorRGB Camera