NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
Andrea Dunn Beltran, Daniel Rho, Marc Niethammer, Roni Sengupta

TL;DR
This paper introduces NFL-BA, a novel bundle adjustment loss that models near-field lighting to improve SLAM performance in dynamic lighting conditions, especially in endoscopy and indoor scenes.
Contribution
NFL-BA explicitly models near-field lighting within bundle adjustment, enhancing SLAM accuracy in environments with dynamic, view-dependent lighting conditions.
Findings
37% improvement in camera tracking for MonoGS
14% improvement for EndoGS
State-of-the-art results on colonoscopy dataset
Abstract
Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models near-field lighting as a part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SLAM can enable autonomous navigation, guidance to…
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Taxonomy
TopicsAugmented Reality Applications · Corneal Surgery and Treatments
