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

arXiv: https://arxiv.org/abs/1605.01923 · HTML: https://mlsanity.com/paper/1605.01923/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2016.

## Built on by (most cited first, 6 of 6)

- [Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments](https://mlsanity.com/paper/2309.00014/) (2023)
- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [Adversarial Neon Beam: A Light-based Physical Attack to DNNs](https://mlsanity.com/paper/2204.00853/) (2022)
- [Auto3R: Automated 3D Reconstruction and Scanning via Data-driven Uncertainty Quantification](https://mlsanity.com/paper/2512.04528/) (2025)
- [On-the-fly Feedback SfM: Online Explore-and-Exploit UAV Photogrammetry with Incremental Mesh Quality-Aware Indicator and Predictive Path Planning](https://mlsanity.com/paper/2512.02375/) (2025)
- [OpenFlyScan: A Quality-Guided Aerial Reconstruction System for Consumer Drones](https://mlsanity.com/paper/2609.24253/) (2026)

## Abstract (arXiv)

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.