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Technical University of Munich

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Photometric Calibration

Online Photometric Calibration

Introduction

Recent direct visual odometry and SLAM algorithms have demonstrated impressive levels of precision. However, they require a photometric camera calibration in order to achieve competitive results. Hence, the respective algorithm cannot be directly applied to an off-the-shelf-camera or to a video sequence acquired with an unknown camera. In this work we propose a method for online photometric calibration which enables to process auto exposure videos with visual odometry precisions that are on par with those of photometrically calibrated videos. Our algorithm recovers the exposure times of consecutive frames, the camera response function, and the attenuation factors of the sensor irradiance due to vignetting. Gain robust KLT feature tracks are used to obtain scene point correspondences as input to a nonlinear optimization framework. We show that our approach can reliably calibrate arbitrary video sequences by evaluating it on datasets for which full photometric ground truth is available. We further show that our calibration can improve the performance of a state-of-the-art direct visual odometry method that works solely on pixel intensities, calibrating for photometric parameters in an online fashion in realtime. For more details please refer to our paper.

Video



Code

You can find our implementation of online photometric calibration at
https://github.com/tum-vision/online_photometric_calibration.

Manual Photometric Calibration

We previously presented methods to calibrate response function and vignette manually with specific calibration sequences. For more information, see https://vision.in.tum.de/data/datasets/mono-dataset.

Datasets

The following datasets include photometric calibration:

The online photometric calibration tool can be used to calibrate other datasets which don't provide photometric calibration information or sequences.

Journal Articles
2018
Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang, D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018.(This paper was also selected by ICRA'18 for presentation at the conference.[arxiv][video][code][project]) [bib] [pdf]ICRA'18 Best Vision Paper Award - Finalist
Conference and Workshop Papers
2016
A Photometrically Calibrated Benchmark For Monocular Visual Odometry (J. Engel, V. Usenko, D. Cremers), In arXiv:1607.02555, 2016. [bib] [pdf]
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