no code implementations • 11 Apr 2024 • Xavier Alameda-Pineda, Angus Addlesee, Daniel Hernández García, Chris Reinke, Soraya Arias, Federica Arrigoni, Alex Auternaud, Lauriane Blavette, Cigdem Beyan, Luis Gomez Camara, Ohad Cohen, Alessandro Conti, Sébastien Dacunha, Christian Dondrup, Yoav Ellinson, Francesco Ferro, Sharon Gannot, Florian Gras, Nancie Gunson, Radu Horaud, Moreno D'Incà, Imad Kimouche, Séverin Lemaignan, Oliver Lemon, Cyril Liotard, Luca Marchionni, Mordehay Moradi, Tomas Pajdla, Maribel Pino, Michal Polic, Matthieu Py, Ariel Rado, Bin Ren, Elisa Ricci, Anne-Sophie Rigaud, Paolo Rota, Marta Romeo, Nicu Sebe, Weronika Sieińska, Pinchas Tandeitnik, Francesco Tonini, Nicolas Turro, Timothée Wintz, Yanchao Yu
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary.
2 code implementations • 20 Feb 2023 • Daniel Barath, Dmytro Mishkin, Michal Polic, Wolfgang Förstner, Jiri Matas
We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D.
no code implementations • CVPR 2023 • Daniel Barath, Dmytro Mishkin, Michal Polic, Wolfgang Förstner, Jiri Matas
We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D.
1 code implementation • 21 Sep 2022 • Martina Dubenova, Anna Zderadickova, Ondrej Kafka, Tomas Pajdla, Michal Polic
Lastly, we describe and improve the mistakes caused by gradient-based comparison between synthetic and query images and publish a new pipeline for simulation of environments with movable objects from the Matterport scans.
1 code implementation • ECCV 2020 • Daniel Barath, Michal Polic, Wolfgang Förstner, Torsten Sattler, Tomas Pajdla, Zuzana Kukelova
The main advantage of such solvers is that their sample size is smaller, e. g., only two instead of four matches are required to estimate a homography.
1 code implementation • CVPR 2020 • Michal Polic, Stanislav Steidl, Cenek Albl, Zuzana Kukelova, Tomas Pajdla
In this paper, we present a new automatic method for camera model selection in large scale SfM that is based on efficient uncertainty evaluation.
no code implementations • ECCV 2018 • Michal Polic, Wolfgang Förstner, Tomas Pajdla
Estimating uncertainty of camera parameters computed in Structure from Motion (SfM) is an important tool for evaluating the quality of the reconstruction and guiding the reconstruction process.