UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction
This addresses the challenge of efficient and high-quality 3D reconstruction for UAV-based applications, representing an incremental improvement over prior methods.
The paper tackles the problem of autonomous image acquisition for 3D reconstruction by predicting reconstruction confidence in real-time without executing multi-view stereo, enabling on-site quality assurance and tailored view planning, and demonstrates it with a UAV in an outdoor scenario.
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.