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

• URL: http://arxiv.org/abs/2410.23522v1
• Date: Thu, 31 Oct 2024 00:08:36 GMT
• Title: LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light
• Authors: Ahalya Ravendran, Mitch Bryson, Donald G. Dansereau,
• Abstract summary: 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.
• Score: 2.048226951354646
• License:
• 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.

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