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no code implementations • ECCV 2020 • Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla

We present a complete classification of minimal problems for generic arrangements of points and lines in space observed partially by three calibrated perspective cameras when each line is incident to at most one point.

1 code implementation • ECCV 2020 • Federica Arrigoni, Luca Magri, Tomas Pajdla

Motion segmentation, i. e., the problem of clustering data in multiple images based on different 3D motions, is an important task for reconstructing and understanding dynamic scenes.

1 code implementation • 1 Jul 2023 • Evgeniy Martyushev, Snehal Bhayani, Tomas Pajdla

The important property of an elimination template is that it applies to all polynomial systems in the family.

1 code implementation • ICCV 2023 • Federica Arrigoni, Tomas Pajdla, Andrea Fusiello

We present an advance in understanding the projective Structure-from-Motion, focusing in particular on the viewing graph: such a graph has cameras as nodes and fundamental matrices as edges.

no code implementations • CVPR 2023 • Petr Hruby, Viktor Korotynskiy, Timothy Duff, Luke Oeding, Marc Pollefeys, Tomas Pajdla, Viktor Larsson

The minimal case for reconstruction requires 13 points in 4 views for both the calibrated and uncalibrated cameras.

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.

no code implementations • 21 Aug 2022 • Aikaterini Adam, Torsten Sattler, Konstantinos Karantzalos, Tomas Pajdla

AR/VR applications and robots need to know when the scene has changed.

1 code implementation • CVPR 2022 • Evgeniy Martyushev, Jana Vrablikova, Tomas Pajdla

For the problem of refractive absolute pose estimation with unknown focal length, we have found a template that is 20 times smaller.

1 code implementation • CVPR 2022 • Petr Hruby, Timothy Duff, Anton Leykin, Tomas Pajdla

The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions.

no code implementations • 24 May 2021 • Petr Hruby, Tomas Pajdla

It is a particular variation of multibody structure from motion, which specializes to two objects only.

no code implementations • 10 May 2021 • Timothy Duff, Viktor Korotynskiy, Tomas Pajdla, Margaret H. Regan

We consider three classical cases--3-point absolute pose, 5-point relative pose, and 4-point homography estimation for calibrated cameras--where the decomposition and symmetries may be naturally understood in terms of the Galois/monodromy group.

1 code implementation • ICCV 2021 • Federica Arrigoni, Andrea Fusiello, Elisa Ricci, Tomas Pajdla

In structure-from-motion the viewing graph is a graph where vertices correspond to cameras and edges represent fundamental matrices.

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.

no code implementations • CVPR 2020 • Cenek Albl, Zuzana Kukelova, Viktor Larsson, Tomas Pajdla, Konrad Schindler

Most consumer cameras are equipped with electronic rolling shutter, leading to image distortions when the camera moves during image capture.

no code implementations • ECCV 2020 • Zuzana Kukelova, Cenek Albl, Akihiro Sugimoto, Konrad Schindler, Tomas Pajdla

The internal geometry of most modern consumer cameras is not adequately described by the perspective projection.

no code implementations • 10 Mar 2020 • Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla

We present a complete classification of minimal problems for generic arrangements of points and lines in space observed partially by three calibrated perspective cameras when each line is incident to at most one point.

no code implementations • ICCV 2019 • Hajime Taira, Ignacio Rocco, Jiri Sedlar, Masatoshi Okutomi, Josef Sivic, Tomas Pajdla, Torsten Sattler, Akihiko Torii

The pose with the largest geometric consistency with the query image, e. g., in the form of an inlier count, is then selected in a second stage.

1 code implementation • ICCV 2019 • Federica Arrigoni, Tomas Pajdla

In this paper we address a classification problem that has not been considered before, namely motion segmentation given pairwise matches only.

4 code implementations • 9 May 2019 • Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, Torsten Sattler

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.

Ranked #8 on Image Matching on IMC PhotoTourism

1 code implementation • 24 Mar 2019 • Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla

We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras.

1 code implementation • 23 Mar 2019 • Ricardo Fabbri, Timothy Duff, Hongyi Fan, Margaret Regan, David da Costa de Pinho, Elias Tsigaridas, Charles Wampler, Jonathan Hauenstein, Benjamin Kimia, Anton Leykin, Tomas Pajdla

We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of i) three points and one line and the novel case of ii) three points and two lines through two of the points.

no code implementations • 30 Dec 2018 • Zuzana Kukelova, Cenek Albl, Akihiro Sugimoto, Tomas Pajdla

Our best 6-point solver, based on the new alternation technique, shows an identical or even better performance than the state-of-the-art R6P solver and is two orders of magnitude faster.

3 code implementations • NeurIPS 2018 • Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic

Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.

Ranked #2 on Semantic correspondence on PF-PASCAL (PCK (weak) metric)

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.

no code implementations • CVPR 2018 • Viktor Larsson, Magnus Oskarsson, Kalle Astrom, Alge Wallis, Zuzana Kukelova, Tomas Pajdla

In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases.

1 code implementation • CVPR 2018 • Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, Akihiko Torii

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map.

no code implementations • 12 Mar 2018 • Viktor Larsson, Magnus Oskarsson, Kalle Åström, Alge Wallis, Zuzana Kukelova, Tomas Pajdla

In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases.

2 code implementations • CVPR 2018 • Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, Fredrik Kahl, Tomas Pajdla

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds.

no code implementations • CVPR 2017 • Torsten Sattler, Akihiko Torii, Josef Sivic, Marc Pollefeys, Hajime Taira, Masatoshi Okutomi, Tomas Pajdla

3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately.

2 code implementations • CVPR 2017 • Cenek Albl, Zuzana Kukelova, Andrew Fitzgibbon, Jan Heller, Matej Smid, Tomas Pajdla

We present new methods for simultaneously estimating camera geometry and time shift from video sequences from multiple unsynchronized cameras.

no code implementations • CVPR 2017 • Zuzana Kukelova, Joe Kileel, Bernd Sturmfels, Tomas Pajdla

We present a new insight into the systematic generation of minimal solvers in computer vision, which leads to smaller and faster solvers.

no code implementations • 6 Oct 2016 • Joe Kileel, Zuzana Kukelova, Tomas Pajdla, Bernd Sturmfels

The distortion varieties of a given projective variety are parametrized by duplicating coordinates and multiplying them with monomials.

no code implementations • CVPR 2016 • Cenek Albl, Zuzana Kukelova, Tomas Pajdla

We compare our R5Pup to the state of the art RS and perspective methods and demonstrate that it outperforms them when vertical direction is known in the range of accuracy available on modern mobile devices.

no code implementations • ICCV 2015 • Zuzana Kukelova, Jan Heller, Martin Bujnak, Andrew Fitzgibbon, Tomas Pajdla

In this paper, we present a new efficient solution to this problem that uses 10 image correspondences.

15 code implementations • CVPR 2016 • Relja Arandjelović, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph.

Ranked #3 on Visual Place Recognition on Mid-Atlantic Ridge

no code implementations • CVPR 2015 • Cenek Albl, Zuzana Kukelova, Tomas Pajdla

Therefore we can use the standard P3P algorithm to estimate camera orientation and to bring the camera rotation matrix close to the identity.

no code implementations • CVPR 2015 • Zuzana Kukelova, Jan Heller, Martin Bujnak, Tomas Pajdla

The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more.

no code implementations • CVPR 2015 • Akihiko Torii, Relja Arandjelovic, Josef Sivic, Masatoshi Okutomi, Tomas Pajdla

We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed.

no code implementations • 13 Feb 2014 • Jan Heller, Didier Henrion, Tomas Pajdla

We show that the method of convex linear matrix inequality (LMI) relaxations can be used to effectively solve these problems and to obtain globally optimal solutions.

no code implementations • CVPR 2013 • Akihiko Torii, Josef Sivic, Tomas Pajdla, Masatoshi Okutomi

Even more importantly, they violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance.

no code implementations • CVPR 2013 • Petr Gronat, Guillaume Obozinski, Josef Sivic, Tomas Pajdla

The aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database.

no code implementations • 25 Apr 2013 • Tanja Schilling, Tomas Pajdla

In this paper, we propose an algebraic approach to upgrade a projective reconstruction to a Euclidean one, and aim at computing the rectifying homography from a minimal number of 9 segments of known length.

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