no code implementations • 13 Dec 2023 • Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
To find the geolocation of a street-view image, cross-view geolocalization (CVGL) methods typically perform image retrieval on a database of georeferenced aerial images and determine the location from the visually most similar match.
1 code implementation • 1 Jun 2023 • Sebastian Bullinger, Florian Fervers, Christoph Bodensteiner, Michael Arens
This allows us to perform a tile specific data augmentation during training and a substitution of pixel predictions with limited context information using data of overlapping tiles during inference.
no code implementations • CVPR 2023 • Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose.
no code implementations • 7 Mar 2022 • Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
1 code implementation • 22 Nov 2021 • Florian Fervers, Timo Breuer, Gregor Stachowiak, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce.
1 code implementation • 4 Feb 2021 • Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry.
2 code implementations • 2 Dec 2020 • Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking.
no code implementations • ECCV 2018 • Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
We apply Structure from Motion techniques to vehicle and background images to determine for each frame camera poses relative to vehicle instances and background structures.
no code implementations • 27 Aug 2018 • Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
We compute the object trajectory by combining object and background camera pose information.
no code implementations • 16 Nov 2017 • Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen
We apply Structure from Motion techniques to object and background images to determine for each frame camera poses relative to object instances and background structures.
no code implementations • 3 Mar 2017 • Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
The evaluation shows that our tracking approach is able to track objects with high relative motions.