Unsupervised Monocular Training Method for Depth Estimation Using Statistical Masks

Recently, unsupervised monocular training methods based on convolutional neural networks have already shown surprisingly progress in improving the accuracy of depth estimation. However, the performance of these methods suffers deeply from problematic pixels such as occluded pixels, low-texture pixels, and so on. In this paper, we introduce a method to a mask by the statistic of error maps for segmenting the problematic pixels. Different from the conventional methods which use additional segmentation networks to classify problematic pixels, we use a multi-task learning architecture to generate identical mask, mean mask, and variance mask for filtering the problematic pixels. Experimental results show that our proposed method has satisfactory performance compared with other relative methods on the KITTI dataset. Moreover, we also apply our method to the UAV dataset VisDrone, and the results also indicate the effectiveness of the method in detecting moving objects.