UAV-Based Autonomous Image Acquisition With Multi-View Stereo Quality Assurance by Confidence Prediction

Christian Mostegel, Markus Rumpler, Friedrich Fraundorfer, Horst Bischof; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 1-10

Abstract


In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario.

Related Material


[pdf]
[bibtex]
@InProceedings{Mostegel_2016_CVPR_Workshops,
author = {Mostegel, Christian and Rumpler, Markus and Fraundorfer, Friedrich and Bischof, Horst},
title = {UAV-Based Autonomous Image Acquisition With Multi-View Stereo Quality Assurance by Confidence Prediction},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}