NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting

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 unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking, 37% for MonoGS and 14% for EndoGS, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement in SLAM performance due to NFL-BA. See results at https://asdunnbe.github.io/NFL-BA/