OpenFlyScan guides drones to improve 3D city models quickly
OpenFlyScan: A Quality-Guided Aerial Reconstruction System for Consumer Drones
RoboticsComputer Vision and Pattern Recognition
Summary
Creating detailed 3D maps of cities using drones can be expensive and slow because mistakes are often found too late. The authors created OpenFlyScan, a system that helps consumer drones spot blurry or missing parts in their 3D maps. It then tells the drone where to fly again to capture better images using a phone app, all without extra hardware. This approach makes 3D city mapping faster and more affordable while improving image quality.
What this means in practice
- •For drone mapping teams: Create higher-quality 3D urban maps using affordable drones with automated guidance to reacquire images where models are unclear.
- •For virtual reality content creators: Produce accurate large-scale city models for simulation environments by integrating drone capture with targeted quality improvements.
Authors
Zhongrui You, Zhen Li, Junli Liu, Zhigang Wang, Bin Zhao
Abstract
3D Gaussian Splatting (3DGS) provides high-fidelity scenes for large-scale embodied simulation, but constructing large-scale urban assets remains constrained by expensive equipment and delayed quality feedback. Preset surveys can leave complex surfaces insufficiently observed, with defects discovered only after reconstruction, requiring return visits and repeated processing. We present OpenFlyScan, a quality-guided aerial reconstruction system for consumer drones that integrates a GS quality model, a reacquisition planner, and a custom-designed mobile app. The model learns from GS rendering errors to predict regional reconstruction quality. Based on these predictions, the planner then generates complementary reacquisition strips to be executed through the app, which also supports automated oblique surveys and data transfer without additional hardware on board. Across real aerial scenes, the model effectively identifies regions that are likely to be poorly reconstructed. In the Expo West field experiment, targeted reacquisition improves PSNR at additional views by 10.95 dB. With consumer drones, OpenFlyScan integrates capture, targeted reacquisition, and reconstruction to support rapid, low-cost urban asset creation. Code and models will be made publicly available at https://openflyscan.github.io/.