用红外图像实现高质量3D重建，还能估温。
TeX-NeRF: Neural Radiance Fields from Pseudo-TeX Vision
- 将红外图像转为伪TeX视觉，映射温度/发射率/纹理到HSV色域。
- 在无可见光条件下重建3D场景，效果媲美高质量RGB图像。
- 首个包含红外与伪TeX图像的3D-TeX数据集，适合夜视/恶劣天气应用。
神经辐射场（NeRF）因其出色的视觉效果受到广泛关注，但现有方法多依赖可见光相机拍摄的RGB图像进行三维场景重建。在黑暗、低光照或恶劣天气等实际场景中，可见光相机失效。为此，我们提出TeX-NeRF，一种仅使用红外图像的三维重建方法。该方法引入物体材料发射率作为先验，通过伪TeX视觉预处理红外图像，并将场景的温度（T）、发射率（e）和纹理（X）分别映射至HSV色彩空间的饱和度（S）、色相（H）和明度（V）通道。利用处理后的图像进行新视角合成，取得了优异效果。此外，我们构建了首个包含红外图像及其对应伪TeX视觉图像的3D-TeX数据集。实验表明，该方法不仅在场景重建质量上达到高质RGB图像水平，还可准确估计场景中物体的温度。
原文摘要 · Abstract (English)
Neural radiance fields (NeRF) has gained significant attention for its exceptional visual effects. However, most existing NeRF methods reconstruct 3D scenes from RGB images captured by visible light cameras. In practical scenarios like darkness, low light, or bad weather, visible light cameras become ineffective. Therefore, we propose TeX-NeRF, a 3D reconstruction method using only infrared images, which introduces the object material emissivity as a priori, preprocesses the infrared images using Pseudo-TeX vision, and maps the temperatures (T), emissivities (e), and textures (X) of the scene into the saturation (S), hue (H), and value (V) channels of the HSV color space, respectively. Novel view synthesis using the processed images has yielded excellent results. Additionally, we introduce 3D-TeX Datasets, the first dataset comprising infrared images and their corresponding Pseudo-TeX vision images. Experiments demonstrate that our method not only matches the quality of scene reconstruction achieved with high-quality RGB images but also provides accurate temperature estimations for objects in the scene.
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