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Recent works have shown the benefit of integrating Conditional Random Fields(CRFs) models into deep architectures for improving pixel-level predictiontasks. Following this line of research, in this paper we introduce a novelapproach for monocular depth estimation. Similarly to previous works, ourmethod employs a continuous CRF to fuse multi-scale information derived fromdifferent layers of a front-end Convolutional Neural Network (CNN). Differentlyfrom past works, our approach benefits from a structured attention model whichautomatically regulates the amount of information transferred betweencorresponding features at different scales. Importantly, the proposed attentionmodel is seamlessly integrated into the CRF, allowing end-to-end training ofthe entire architecture. Our extensive experimental evaluation demonstrates theeffectiveness of the proposed method which is competitive with previous methodson the KITTI benchmark and outperforms the state of the art on the NYU Depth V2dataset.

Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation