# Deep multi-scale architectures for monocular depth estimation

arXiv: https://arxiv.org/abs/1806.03051 · HTML: https://mlsanity.com/paper/1806.03051/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation](https://mlsanity.com/paper/2107.13800/) (2021)
- [InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation](https://mlsanity.com/paper/2309.13516/) (2023)

## Abstract (arXiv)

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.