Mean-Variance Loss for Monocular Depth Estimation

Monocular depth estimation is a widely studied computer vision problem with a vast variety of applications. In this paper, we formulate it as a pixel-wise classification task and use a mean-variance loss for robust depth estimation via distribution learning. More precisely, the mean-variance loss is composed of a mean loss that penalizes the difference between the mean of predicted depth distribution and the ground-truth depth, and a variance loss that penalizes the variance of predicted depth distribution to obtain a more focused distribution. The mean-variance loss is jointly trained with the soft-max loss to supervise a Deep Convolutional Neural Networks (DCNN) for depth estimation. Experimental results on the NYUDv2 dataset show that the proposed method outperforms previous state-of-the-art approaches.