Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

Kurzfassung

Learned confidence measures gain increasing impor- tance 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, flexi- ble, 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 reason- ing about contradictions and consistencies between multi- ple 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 boost- ing 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.