  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.
