Thermal-NeRF: Neural Radiance Fields from an Infrared Camera
Tianxiang Ye Qi Wu Junyuan Deng Guoqing Liu Liu Liu Songpengcheng Xia Liang Pang Wenxian Yu Ling Pei
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.