Search Results for author: Michal Polic

Found 7 papers, 4 papers with code

A Large Scale Homography Benchmark

2 code implementations20 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.

Homography Estimation Surface Normal Estimation

A Large-Scale Homography Benchmark

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.

Homography Estimation Surface Normal Estimation

D-InLoc++: Indoor Localization in Dynamic Environments

1 code implementation21 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.

Indoor Localization Pose Estimation +2

Making Affine Correspondences Work in Camera Geometry Computation

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.

Homography Estimation valid

Uncertainty Based Camera Model Selection

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.

Model Selection

Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction

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.

3D Reconstruction

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