Large-Scale Visual Localization

The tutorial covers the task of visual localization, i.e., the problem of estimating the position and orientation from which a given image was taken. The tutorial’s scope includes cases with different spatial/geographical extent, small indoor/outdoor scenes, city-level, and world-level, and localization under changing conditions. In the coarse localization regime, the task is typically handled via retrieval approaches, which is covered in the first part of the tutorial. A typical use case is the following: Given a database of geo-tagged images, the goal is to determine the place depicted in a new query image. Traditionally, this problem is solved by transferring the geo-tag of the most similar database image to the query. The major focus of this part is on the visual representation models used for retrieval, where we include both classical feature-based and recent deep learning-based approaches. The 2nd and 3rd part of the tutorial encompass methods for precise localization with features-based and deep learning approaches, respectively. A typical use-case for these algorithms is to estimate the full 6 Degree-of-Freedom (6DOF) pose of a query image, i.e., the position and orientation from which the image was taken, for applications such as robotics, autonomous vehicles (self-driving cars), Augmented / Mixed / Virtual Reality, loop closure detection in SLAM, and Structure-from-Motion. The final part will cover existing datasets, including their limitations.We provide links to publicly available source code for the discussed approaches.