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.

在这项工作中，我们提出了一种新颖的方法来对多视图立体声（MVS）的深度图计算进行优先级排序，从而以较低的计算成本获得高质量和完整性的紧凑型3D点云。我们的优先级排序方法在执行MVS算法之前运行，包括两个步骤。第一步，我们旨在为每个视图找到一组良好的匹配伙伴。在第二步中，我们根据得到的视图簇（即具有匹配伙伴的关键视图）对实现所需质量参数（如完整性，地面分辨率和准确性）的影响进行排名。除了几何分析之外，我们还使用一种新颖的机器学习技术来训练置信度预测器。该置信度预测器的目的是基于RGB图像和图像星座图，针对一种特定的MVS算法，估计每个图像中每个像素成功进行深度重建的机会。底层的机器学习技术不需要任何基础知识或经过人工标记的数据即可进行训练，而可以改编深度图融合中的思想以提供监控信号。经过训练的置信度预测器使我们能够评估图像星座的质量及其对最终3D重建的潜在影响，从而为我们的优先排序方法奠定了坚实的基础。因此，在我们的实验中，仅使用5％的可用图像作为关键视图，我们就可以达到最大可实现质量的70％以上。为了评估我们在不同领域内和跨领域的方法，