image-based rendering of diffuse, specular and glossy surfaces from a single image
In this paper, we present a new method to recover an approximation of the bidirectional reflectance distribution function (BRDF) of the surfaces present in a real scene. This is done from a single photograph and a 3D geometric model of the scene. The result is a full model of the reflectance properties of all surfaces, which can be rendered under novel illumination conditions with, for example, viewpoint modification and the addition of new synthetic objects. Our technique produces a reflectance model using a small number of parameters. These parameters nevertheless approximate the BRDF and allow the recovery of the photometric properties of diffuse, specular, isotropic or anisotropic textured objects. The input data are a geometric model of the scene including the light source positions and the camera properties, and a single image captured using this camera. Our algorithm generates a new synthetic image using classic rendering techniques, and a lambertian hypothesis about the reflectance model of the surfaces. Then, it iteratively compares the original image to the new one, and chooses a more complex reflectance model if the difference between the two images is greater than a user-defined threshold. We present several synthetic images that are compared to the original ones, and some possible applications in augmented reality.
image-based rendering imaging imaging rendering diffuse surface paper method approximation reflectance distributed functional 3d geometric modeling model illumination examples object reflection model recovery input data lighting camera algorithm complexity threshold applications augmented reality radiosity brdf models radiance user rerendering reflectance recovery inverse rendering image global illumination