Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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 beten the two images is greater than a user-defined threshold.   present several synthetic images that are compared to the original ones, and some possible applications in augmented reality.
['Samuel Boivin', 'André Gagalowicz']
I am writing a research paper on a idea Image-based rendering of diffuse, specular and glossy surfaces from a single image
[{"from": "human", "value": "I am writing a research paper on a idea Image-based rendering of diffuse, specular and glossy surfaces from a single image"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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 beten the two images is greater than a user-defined threshold.   present several synthetic images that are compared to the original ones, and some possible applications in augmented reality."}]