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.

本文介绍了一种自主获取近距离高分辨率图像的系统，以最大化后续 3D 重建的质量，该系统使用已获取的图像预测密集多视角立体视觉方法的输出置信度，用于现场质量保证和计划更多视角。我们使用无人机在具有挑战性的户外场景中演示了我们方法的能力。