Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network

TL;DR

This paper introduces a multi-range fully convolutional neural network for real-time depth inference from monocular stabilized videos, specifically tailored for UAV applications in outdoor environments, leveraging sensor data for improved accuracy.

Contribution

The paper presents a novel multi-range neural network architecture that enhances depth inference accuracy from monocular UAV videos by integrating flight sensor data.

Findings

Improved depth inference accuracy with multi-range architecture.

Effective on both synthetic and real UAV data.

Quantitative results demonstrate the method's robustness.

Abstract

Using a neural network architecture for depth map inference from monocular stabilized videos with application to UAV videos in rigid scenes, we propose a multi-range architecture for unconstrained UAV flight, leveraging flight data from sensors to make accurate depth maps for uncluttered outdoor environment. We try our algorithm on both synthetic scenes and real UAV flight data. Quantitative results are given for synthetic scenes with a slightly noisy orientation, and show that our multi-range architecture improves depth inference. Along with this article is a video that present our results more thoroughly.