Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference.

•We propose a deep end-to-end learning framework to monocular depth estimation by recasting it as a multi-category classification task, where both dilated convolution and hierarchical feature fusion are used to learn the scale-aware depth cues.•Our network is able to output the probability distribution among different depth labels. We propose a soft-weighted-sum inference, which could reduce the influence of quantization error and improve the robustness.•Our method achieves the state-of-the-art performance on both indoor and outdoor benchmarking datasets, Make3D, NYU V2 and KITTI dataset.