  Monocular depth estimation is a challenging task in complex compositions
depicting multiple objects of diverse scales. Albeit the recent great progress
thanks to the deep convolutional neural networks (CNNs), the state-of-the-art
monocular depth estimation methods still fall short to handle such real-world
challenging scenarios. In this paper, we propose a deep end-to-end learning
framework to tackle these challenges, which learns the direct mapping from a
color image to the corresponding depth map. First, we represent monocular depth
estimation as a multi-category dense labeling task by contrast to the
regression based formulation. In this way, we could build upon the recent
progress in dense labeling such as semantic segmentation. Second, we fuse
different side-outputs from our front-end dilated convolutional neural network
in a hierarchical way to exploit the multi-scale depth cues for depth
estimation, which is critical to achieve scale-aware depth estimation. Third,
we propose to utilize soft-weighted-sum inference instead of the hard-max
inference, transforming the discretized depth score to continuous depth value.
Thus, we reduce the influence of quantization error and improve the robustness
of our method. Extensive experiments on the NYU Depth V2 and KITTI datasets
show the superiority of our method compared with current state-of-the-art
methods. Furthermore, experiments on the NYU V2 dataset reveal that our model
is able to learn the probability distribution of depth.
