Deep multi-scale architectures for monocular depth estimation
This work addresses depth estimation from single images for computer vision applications, but it is incremental as it builds on existing multi-scale methods.
The paper tackled monocular depth estimation by investigating four deep CNN architectures that explicitly use multi-scale features, achieving state-of-the-art performance on the NYU Depth dataset with improved accuracy and qualitative depth maps.
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images. More precisely, the paper investigates four different deep CNN architectures, designed to explicitly make use of multi-scale features along the network, and compare them to a state-of-the-art single-scale approach. The paper also shows that involving multi-scale features in depth estimation not only improves the performance in terms of accuracy, but also gives qualitatively better depth maps. Experiments are done on the widely used NYU Depth dataset, on which the proposed method achieves state-of-the-art performance.