Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

TL;DR

This paper introduces a self-supervised method for monocular depth estimation from aerial imagery that does not require annotated data, using only image sequences to learn depth and pose, suitable for real-time applications.

Contribution

The paper presents a novel self-supervised approach for aerial monocular depth estimation that jointly learns depth and pose from single-camera sequences, enabling real-time processing without annotated data.

Findings

Achieves up to 93.5% accuracy at δ1.25

Demonstrates good generalization to unseen data

Provides useful initializations for image matching methods

Abstract

Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy {\delta}1.25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions.