针对动态近场光照下的SLAM性能下降问题，提出新型光束法平差损失函数。
NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
- 将近场光照建模为光束法平差损失的一部分，显式处理视点依赖的阴影。
- 在结肠镜数据集上，相机追踪精度提升37%（MonoGS）和14%（EndoGS）。
- 适用于内窥镜、地下机器人等无外部光源场景，适合医疗导航与自主探索。
同时定位与地图构建（SLAM）系统通常假设光照静态且遥远；然而，在内窥镜、地下机器人及坍塌环境搜救等真实场景中，需在无外部照明条件下使用同置的灯光与摄像头工作。此时动态近场光照会产生强烈且视角依赖的阴影，严重降低SLAM性能。本文提出近场光照光束法平差损失（NFL-BA），将近场光照作为光束法平差损失的一部分进行显式建模，显著提升动态光照下场景的建模效果。NFL-BA可集成至基于神经渲染的SLAM系统，支持隐式或显式场景表示。实验主要聚焦于内窥镜场景，该场景中SLAM可实现自主导航、引导至未探查区域、盲区检测与三维可视化，显著改善患者预后与诊疗体验。将原有光度光束法平差损失替换为NFL-BA后，相机追踪性能显著提升：在MonoGS上提高37%，在EndoGS上提高14%，并在C3VD结肠镜数据集上达到当前最优表现。进一步在手机闪光灯拍摄的室内场景中验证，同样展现显著性能提升。
原文摘要 · Abstract (English)
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/
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