F
Fredrik Kahl
Researcher at Chalmers University of Technology
Publications - 215
Citations - 7567
Fredrik Kahl is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Motion estimation & Image segmentation. The author has an hindex of 44, co-authored 204 publications receiving 6272 citations. Previous affiliations of Fredrik Kahl include University of California, San Diego & Australian National University.
Papers
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Proceedings ArticleDOI
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Torsten Sattler,Will Maddern,Carl Toft,Akihiko Torii,Lars Hammarstrand,Erik Stenborg,Daniel Safari,Daniel Safari,Masatoshi Okutomi,Marc Pollefeys,Marc Pollefeys,Josef Sivic,Fredrik Kahl,Fredrik Kahl,Tomas Pajdla +14 more
TL;DR: This paper introduces the first benchmark datasets specifically designed for analyzing the impact of day-night changes, weather and seasonal variations, as well as sequence-based localization approaches and the need for better local features on visual localization.
Journal ArticleDOI
Multiple-View Geometry Under the {$L_\infty$}-Norm
Fredrik Kahl,Richard Hartley +1 more
TL;DR: A variety of structure and motion problems, for example, triangulation, camera resectioning, and homography estimation, can be recast as quasi-convex optimization problems within this framework and can be efficiently solved using second-order cone programming (SOCP), which is a standard technique in convex optimization.
Proceedings ArticleDOI
Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions
TL;DR: This paper presents a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor that is more accurate and robust than the iterated closest point algorithm (ICP) used by KinectFusion, and yields often a comparable accuracy at much higher speed to feature-based bundle adjustment methods such asRGB-D SLAM.
Journal ArticleDOI
Global Optimization through Rotation Space Search
Richard Hartley,Fredrik Kahl +1 more
TL;DR: A method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors.
Journal ArticleDOI
City-Scale Localization for Cameras with Known Vertical Direction
TL;DR: This work considers the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known, and extends accurate approximations and fast polynomial solvers to camera pose estimation.