We present a process for estimating spatially-varying surface reflectance of a complex scene observed under natural illumination conditions. The process uses a laser-scanned model of the scene's geometry, a set of digital images viewing the scene's surfaces under a variety of natural illumination conditions, and a set of corresponding measurements of the scene's incident illumination in each photograph. The process then employs an iterative inverse global illumination technique to compute surface colors for the scene which, when rendered under the recorded illumination conditions, best reproduce the scene's appearance in the photographs. In our process we measure BRDFs of representative surfaces in the scene to better model the non-Lambertian surface reflectance. Our process uses a novel lighting measurement apparatus to record the full dynamic range of both sunlit and cloudy natural illumination conditions. We employ Monte-Carlo global illumination, multiresolution geometry, and a texture atlas system to perform inverse global illumination on the scene. The result is a lighting-independent model of the scene that can be re-illuminated under any form of lighting. We demonstrate the process on a real-world archaeological site, showing that the technique can produce novel illumination renderings consistent with real photographs as well as reflectance properties that are consistent with ground-truth reflectance measurements.
