  Deep neural networks have recently thrived on single image depth estimation.
That being said, current developments on this topic highlight an apparent
compromise between accuracy and network size. This work proposes an accurate
and lightweight framework for monocular depth estimation based on a
self-attention mechanism stemming from salient point detection. Specifically,
we utilize a sparse set of keypoints to train a FuSaNet model that consists of
two major components: Fusion-Net and Saliency-Net. In addition, we introduce a
normalized Hessian loss term invariant to scaling and shear along the depth
direction, which is shown to substantially improve the accuracy. The proposed
method achieves state-of-the-art results on NYU-Depth-v2 and KITTI while using
3.1-38.4 times smaller model in terms of the number of parameters than baseline
approaches. Experiments on the SUN-RGBD further demonstrate the
generalizability of the proposed method.
