Deep multi-scale architectures for monocular depth estimation

TL;DR

This paper investigates the impact of multi-scale features in deep CNN architectures for monocular depth estimation, demonstrating improved accuracy and depth map quality on the NYU Depth dataset.

Contribution

It introduces and compares four multi-scale CNN architectures, showing their superiority over single-scale models in depth estimation tasks.

Findings

Multi-scale architectures outperform single-scale models.

Involving multi-scale features improves depth map quality.

Achieves state-of-the-art results on NYU Depth dataset.

Abstract

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.