no code implementations • ECCV 2020 • Marcel Geppert, Viktor Larsson, Pablo Speciale, Johannes L. Schönberger, Marc Pollefeys
The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns.
no code implementations • ECCV 2020 • Viktor Larsson, Nicolas Zobernig, Kasim Taskin, Marc Pollefeys
In this paper we consider the problem of Structure-from-Motion from images with unknown intrinsic calibration.
1 code implementation • ECCV 2020 • Luca Cavalli, Viktor Larsson, Martin Ralf Oswald, Torsten Sattler, Marc Pollefeys
As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, have been proposed over the last decades.
no code implementations • 13 Jan 2024 • Felix Rydell, Angélica Torres, Viktor Larsson
Many problems in computer vision can be formulated as geometric estimation problems, i. e. given a collection of measurements (e. g. point correspondences) we wish to fit a model (e. g. an essential matrix) that agrees with our observations.
1 code implementation • 26 Jul 2023 • Luca Cavalli, Daniel Barath, Marc Pollefeys, Viktor Larsson
The proposed attention mechanism and one-step transformer provide an adaptive behavior that enhances the performance of RANSAC, making it a more effective tool for robust estimation.
1 code implementation • ICCV 2023 • Rémi Pautrat, Iago Suárez, Yifan Yu, Marc Pollefeys, Viktor Larsson
Line segments are powerful features complementary to points.
1 code implementation • CVPR 2023 • Shaohui Liu, Yifan Yu, Rémi Pautrat, Marc Pollefeys, Viktor Larsson
In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements.
1 code implementation • CVPR 2023 • Ganlin Zhang, Viktor Larsson, Daniel Barath
In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines.
no code implementations • 7 Feb 2023 • Zihan Zhu, Songyou Peng, Viktor Larsson, Zhaopeng Cui, Martin R. Oswald, Andreas Geiger, Marc Pollefeys
Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM.
1 code implementation • CVPR 2023 • Yaqing Ding, Jian Yang, Viktor Larsson, Carl Olsson, Kalle Åström
One of the classical multi-view geometry problems is the so called P3P problem, where the absolute pose of a calibrated camera is determined from three 2D-to-3D correspondences.
no code implementations • ICCV 2023 • Linfei Pan, Johannes L. Schönberger, Viktor Larsson, Marc Pollefeys
Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps.
no code implementations • ICCV 2023 • Yaqing Ding, Chiang-Heng Chien, Viktor Larsson, Karl Åström, Benjamin Kimia
For a generalized (or non-central) camera model, the minimal problem for two views of six points has efficient solvers.
no code implementations • ICCV 2023 • Mikhail Terekhov, Viktor Larsson
In this paper we introduce the Tangent Sampson error, which is a generalization of the classical Sampson error in two-view geometry that allows for arbitrary central camera models.
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 • CVPR 2023 • Rémi Pautrat, Daniel Barath, Viktor Larsson, Martin R. Oswald, Marc Pollefeys
Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines.
no code implementations • 21 Nov 2022 • Ahad Hamednia, Jimmy Forsman, Nikolce Murgovski, Viktor Larsson, Jonas Fredriksson
This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate.
no code implementations • 19 Oct 2022 • Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger, Pablo Speciale, Lukas Gruber, Viktor Larsson, Ondrej Miksik, Marc Pollefeys
To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
no code implementations • 7 Oct 2022 • Ahad Hamednia, Victor Hanson, Jiaming Zhao, Nikolce Murgovski, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson, Jonas Fredriksson
This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips.
no code implementations • 29 Sep 2022 • Snehal Bhayani, Viktor Larsson, Torsten Sattler, Janne Heikkila, Zuzana Kukelova
In this paper we study the problem of estimating the semi-generalized pose of a partially calibrated camera, i. e., the pose of a perspective camera with unknown focal length w. r. t.
1 code implementation • 27 Sep 2022 • Hao Dong, Xieyuanli Chen, Mihai Dusmanu, Viktor Larsson, Marc Pollefeys, Cyrill Stachniss
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.
no code implementations • 3 May 2022 • Ahad Hamednia, Nikolce Murgovski, Jonas Fredriksson, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson
The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors.
no code implementations • CVPR 2022 • Linfei Pan, Marc Pollefeys, Viktor Larsson
Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration.
no code implementations • CVPR 2022 • Marcel Geppert, Viktor Larsson, Johannes L. Schönberger, Marc Pollefeys
We propose a principled approach overcoming these limitations, based on two observations.
1 code implementation • CVPR 2022 • Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, Marc Pollefeys
Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM).
1 code implementation • ICCV 2021 • Philipp Lindenberger, Paul-Edouard Sarlin, Viktor Larsson, Marc Pollefeys
Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction.
no code implementations • CVPR 2021 • Marcel Geppert, Viktor Larsson, Pablo Speciale, Johannes L. Schonberger, Marc Pollefeys
In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem.
no code implementations • CVPR 2021 • Carl Olsson, Viktor Larsson, Fredrik Kahl
In this paper we study structure from motion problems for 1D radial cameras.
1 code implementation • CVPR 2021 • Rémi Pautrat, Juan-Ting Lin, Viktor Larsson, Martin R. Oswald, Marc Pollefeys
We thus hereby introduce the first joint detection and description of line segments in a single deep network.
no code implementations • ICCV 2021 • Peidong Liu, Xingxing Zuo, Viktor Larsson, Marc Pollefeys
Motion blur is one of the major challenges remaining for visual odometry methods.
2 code implementations • CVPR 2021 • Paul-Edouard Sarlin, Ajaykumar Unagar, Måns Larsson, Hugo Germain, Carl Toft, Viktor Larsson, Marc Pollefeys, Vincent Lepetit, Lars Hammarstrand, Fredrik Kahl, Torsten Sattler
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
1 code implementation • 6 Feb 2021 • Dalia El Badawy, Viktor Larsson, Marc Pollefeys, Ivan Dokmanić
We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known.
no code implementations • 6 Jan 2021 • Lucas Brynte, Viktor Larsson, José Pedro Iglesias, Carl Olsson, Fredrik Kahl
In studying the empirical performance we note that there are few failure cases reported in the literature, in particular for estimation problems with a single rotation, motivating us to gain further theoretical understanding.
no code implementations • ICCV 2021 • Viktor Larsson, Marc Pollefeys, Magnus Oskarsson
In this paper we consider the epipolar geometry between orthographic and perspective cameras.
1 code implementation • ECCV 2020 • Yukai Lin, Viktor Larsson, Marcel Geppert, Zuzana Kukelova, Marc Pollefeys, Torsten Sattler
In particular, our approach is more robust than the naive approach of first estimating intrinsic parameters and pose per camera before refining the extrinsic parameters of the system.
1 code implementation • ECCV 2020 • Rémi Pautrat, Viktor Larsson, Martin R. Oswald, Marc Pollefeys
To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors.
3 code implementations • 7 Jun 2020 • Luca Cavalli, Viktor Larsson, Martin Ralf Oswald, Torsten Sattler, Marc Pollefeys
Local feature matching is a critical component of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization.
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.
1 code implementation • 24 Apr 2020 • Bo Li, Viktor Larsson
Minimal problems in computer vision raise the demand of generating efficient automatic solvers for polynomial equation systems.
2 code implementations • 5 Dec 2019 • Thomas Schöps, Viktor Larsson, Marc Pollefeys, Torsten Sattler
In contrast, generic camera models allow for very accurate calibration due to their flexibility.
1 code implementation • 4 Nov 2019 • James Pritts, Zuzana Kukelova, Viktor Larsson, Yaroslava Lochman, Ondřej Chum
This paper introduces minimal solvers that jointly solve for radial lens undistortion and affine-rectification using local features extracted from the image of coplanar translated and reflected scene texture, which is common in man-made environments.
no code implementations • ICCV 2019 • Viktor Larsson, Torsten Sattler, Zuzana Kukelova, Marc Pollefeys
In this paper we aim to fill this gap in the literature by proposing the first minimal solvers which can jointly estimate distortion models together with camera pose.
1 code implementation • 25 Jul 2019 • James Pritts, Zuzana Kukelova, Viktor Larsson, Yaroslava Lochman, Ondřej Chum
The proposed solvers use the affine invariant that coplanar repeats have the same scale in rectified space.
1 code implementation • 16 Jul 2018 • James Pritts, Zuzana Kukelova, Viktor Larsson, Ondrej Chum
This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features.
no code implementations • CVPR 2018 • Viktor Larsson, Zuzana Kukelova, Yinqiang Zheng
To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization.
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.
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.
1 code implementation • CVPR 2018 • James Pritts, Zuzana Kukelova, Viktor Larsson, Ondrej Chum
The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion.
no code implementations • ICCV 2017 • Viktor Larsson, Kalle Astrom, Magnus Oskarsson
In this paper we present a new method for creating polynomial solvers for problems where a (possibly infinite) subset of the solutions are undesirable or uninteresting.
no code implementations • ICCV 2017 • Viktor Larsson, Zuzana Kukelova, Yinqiang Zheng
In this paper we present new techniques for constructing compact and robust minimal solvers for absolute pose estimation.
no code implementations • CVPR 2017 • Viktor Larsson, Carl Olsson
This imposes constraints on the matrix elements which allow for estimation of missing entries.
no code implementations • CVPR 2017 • Viktor Larsson, Kalle Astrom, Magnus Oskarsson
In this paper we study the problem of automatically generating polynomial solvers for minimal problems.
no code implementations • CVPR 2016 • Johan Fredriksson, Viktor Larsson, Carl Olsson, Fredrik Kahl
Previous work on estimating the epipolar geometry of two views relies on being able to reliably match feature points based on appearance.
no code implementations • CVPR 2015 • Johan Fredriksson, Viktor Larsson, Carl Olsson
Outliers pose a problem in all real structure from motion systems.