eivan / multiview-LAFs-correction

Code for our BMVC2019 paper: "Optimal Multi-view Correction of Local Affine Frames" by Ivan Eichhardt and Daniel Barath.

Optimal Multi-view Correction of Local Affine Frames

Code for our BMVC2019 paper: "Optimal Multi-view Correction of Local Affine Frames" by Ivan Eichhardt and Daniel Barath. Further available resources: paper, supplementary material.

Cite it as

@InProceedings{Eichhardt_Barath_2019_BMVC,
	author = {Eichhardt, Ivan and Barath, Daniel},
	title = {Optimal Multi-view Correction of Local Affine Frames},
	booktitle = {British Machine Vision Conference (BMVC)},
	month = {September},
	year = {2019}
}

Introduction

A method is proposed for correcting the parameters of a sequence of detected local affine frames through multiple views. The technique requires the epipolar geometry to be pre-estimated between each image pair. It exploits the constraints which the camera movement implies, in order to apply a closed-form correction to the parameters of the input affinities. Also, it is shown that the rotations and scales obtained by partially affine-covariant detectors, e.g. AKAZE or SIFT, can be upgraded to be full affine frames by the proposed algorithm. It is validated both in synthetic experiments and on publicly available real-world datasets that the method almost always improves the output of the evaluated affine-covariant feature detectors. As a by-product, these detectors are compared and the ones obtaining the most accurate affine frames are reported. To demonstrate the applicability in real-world scenarios, we show that the proposed technique improves the accuracy of pose estimation for a camera rig, surface normal and homography estimation.

Building

See BUILD

Dependencies:

• Eigen3
• OpenMVG (a modified version) (optional, for specific samples)

OpenMVG examples (optional)

Follow the Wiki of the OpenMVG examples for a description of the OpenMVG-based tools provided with this repository.