Learned confidence measures gain increasing importance for outlier removal
and quality improvement in stereo vision. However, acquiring the necessary
training data is typically a tedious and time consuming task that involves
manual interaction, active sensing devices and/or synthetic scenes. To overcome
this problem, we propose a new, flexible, and scalable way for generating
training data that only requires a set of stereo images as input. The key idea
of our approach is to use different view points for reasoning about
contradictions and consistencies between multiple depth maps generated with the
same stereo algorithm. This enables us to generate a huge amount of training
data in a fully automated manner. Among other experiments, we demonstrate the
potential of our approach by boosting the performance of three learned
confidence measures on the KITTI2012 dataset by simply training them on a vast
amount of automatically generated training data rather than a limited amount of
laser ground truth data.

提出了一种基于立体图像和深度图像的新方法，用于产生训练数据，以用于学习置信度测量，以提高异常值去除和在立体视觉中的质量改进。该方法可在完全自动化方式下生成大量的训练数据，并且在 KITTI2012 数据集上得到了证明。