mlsanityData to 14 Oct 2026

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

arXiv 2410.23522 ↗First posted 31 Oct 2024cs.CV cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 31 Oct 2024.

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17 of 30 references.

References · 17

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5 more
  1. 2020MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videoscited by 25
  2. 2021Dogfight: Detecting Drones from Drones Videoscited by 18
  3. 2020Domain-Aware Unsupervised Hyperspectral Reconstruction for Aerial Image Dehazingcited by 6
  4. 2021Burst Imaging for Light-Constrained Structure-From-Motioncited by 3
  5. 2022BuFF: Burst Feature Finder for Light-Constrained 3D Reconstructioncited by 1

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