LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light

October 31, 2024 · Entered Twilight · 🏛 arXiv.org

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Repo contents: README.md, assets, confidence_maps.sh, datasets, extract.py, extract_burst.sh, imgs, matching_performance.png, models, nets, tools, train.py, utils, visualise_confidence_maps.py

Authors Ahalya Ravendran, Mitch Bryson, Donald G. Dansereau arXiv ID 2410.23522 Category cs.CV: Computer Vision Cross-listed cs.RO Citations 0 Venue arXiv.org Repository https://github.com/RoboticImaging/LBurst ⭐ 6 Last Checked 2 months ago

Abstract

Drones have revolutionized the fields of aerial imaging, mapping, and disaster recovery. However, the deployment of drones in low-light conditions is constrained by the image quality produced by their on-board cameras. In this paper, we present a learning architecture for improving 3D reconstructions in low-light conditions by finding features in a burst. Our approach enhances visual reconstruction by detecting and describing high quality true features and less spurious features in low signal-to-noise ratio images. We demonstrate that our method is capable of handling challenging scenes in millilux illumination, making it a significant step towards drones operating at night and in extremely low-light applications such as underground mining and search and rescue operations.