Research Guide for Depth Estimation with Deep Learning

This paper proposes a fully convolutional architecture to address the problem of estimating the depth map of a scene given an RGB image. Modeling of the ambiguous mapping between monocular images and depth maps is done via residual learning. The reverse Huber loss is used for optimization. The model runs in real-time on images or videos. The approach proposed in this paper uses a CNN for depth estimation.