Abstract: Image-based localization refers to a problem where the camera position and orientation for a given query image is computed with respect to a known visual 3D map of the scene. This problem is relevant for applications such as robot self-localization, pedestrian navigation, and augmented reality. Another related problem is the relative pose estimation between two camera views which is required for computing image-based 3D models from a collection of 2D images. Traditionally both of these problems have been approached by using hand-crafted local image features and descriptors, such as the widely used SIFT keypoint detector. However, recently several deep learning based localization approaches have been proposed. They omit local feature matching and directly try to regress the camera pose. In this presentation, we will describe an overview of the problem area and explore some recent deep learning based approaches. We will also present some of our own recent results in this area.

Speaker: Juho Kannala

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University