UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction

TL;DR

This paper introduces an autonomous UAV system that predicts reconstruction confidence from images to optimize image acquisition for high-quality 3D modeling, without needing external ground truth data.

Contribution

The novel contribution is a real-time confidence prediction module that guides UAV image acquisition for improved 3D reconstruction quality, unlike prior methods.

Findings

Real-time confidence prediction enables better planning of image capture.

The system improves 3D reconstruction coverage and accuracy.

It operates effectively without external ground truth data.

Abstract

In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario.