In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant reliance on RGB imaging presupposes ideal lighting conditions: a premise frequently unmet in robotic applications plagued by poor lighting or visual obstructions. This limitation overlooks the capabilities of infrared (IR) cameras, which excel in low-light detection and present a robust alternative under such adverse scenarios. To tackle these issues, we introduce Thermal-NeRF, the first method that estimates a volumetric scene representation in the form of a NeRF solely from IR imaging. By leveraging a thermal mapping and structural thermal constraint derived from the thermal characteristics of IR imaging, our method showcasing unparalleled proficiency in recovering NeRFs in visually degraded scenes where RGB-based methods fall short. We conduct extensive experiments to demonstrate that Thermal-NeRF can achieve superior quality compared to existing methods. Furthermore, we contribute a dataset for IR-based NeRF applications, paving the way for future research in IR NeRF reconstruction.

翻译：近年来，神经辐射场（NeRF）在编码高细节三维几何与环境外观方面展现出显著潜力，被视为传统三维场景重建显式表示的有力替代方案。然而，其通常依赖RGB成像的前提假设存在理想光照条件——这一假设在机器人应用中常因光照不足或视觉遮挡而无法满足。该局限性忽视了红外（IR）相机在弱光检测中的优势，此类相机在上述恶劣场景下可提供鲁棒替代方案。为解决这些问题，我们提出Thermal-NeRF——首个纯依赖红外成像估计体场景表示（NeRF形式）的方法。通过利用红外成像的热特性推导热映射与结构热约束，所提方法在RGB方法难以处理的视觉退化场景中展现出无与伦比的NeRF重建能力。大量实验证明Thermal-NeRF可达到优于现有方法的重建质量。此外，我们贡献了一个基于红外NeRF应用的数据集，为红外NeRF重建的未来研究铺平道路。