Apply Fuzzy Mask to Improve Monocular Depth Estimation.

A fuzzy mask applied to pixel-wise dissimilarity weighting is proposed to improve the monocular depth estimation in this study. The parameters in the monocular depth estimation model are learned unsupervised through the image reconstruction of binocular images. The significant reconstructed dissimilarity, which is challenging to reduce, always occurs at pixels outside the binocular overlap. The fuzzy mask is designed based on the binocular overlap to adjust the weight of the dissimilarity for each pixel. More than 68% of pixels with significant dissimilarity outside binocular overlap are suppressed with weights less than 0.5. The model with the proposed fuzzy mask would focus on learning the depth estimation for pixels within binocular overlap. Experiments on the KITTI dataset show that the inference of the fuzzy mask only increases the training time of the model by less than 1%, while the number of pixels whose depth is accurately estimated enhances, and the monocular depth estimation also improves. [ABSTRACT FROM AUTHOR]