  In this work, we propose a novel approach to prioritize the depth map
computation of multi-view stereo (MVS) to obtain compact 3D point clouds of
high quality and completeness at low computational cost. Our prioritization
approach operates before the MVS algorithm is executed and consists of two
steps. In the first step, we aim to find a good set of matching partners for
each view. In the second step, we rank the resulting view clusters (i.e. key
views with matching partners) according to their impact on the fulfillment of
desired quality parameters such as completeness, ground resolution and
accuracy. Additional to geometric analysis, we use a novel machine learning
technique for training a confidence predictor. The purpose of this confidence
predictor is to estimate the chances of a successful depth reconstruction for
each pixel in each image for one specific MVS algorithm based on the RGB images
and the image constellation. The underlying machine learning technique does not
require any ground truth or manually labeled data for training, but instead
adapts ideas from depth map fusion for providing a supervision signal. The
trained confidence predictor allows us to evaluate the quality of image
constellations and their potential impact to the resulting 3D reconstruction
and thus builds a solid foundation for our prioritization approach. In our
experiments, we are thus able to reach more than 70% of the maximal reachable
quality fulfillment using only 5% of the available images as key views. For
evaluating our approach within and across different domains, we use two
completely different scenarios, i.e. cultural heritage preservation and
reconstruction of single family houses.
