Samuel Boivin
ABSTRACT
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.
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Index Terms
- Image-based rendering of diffuse, specular and glossy surfaces from a single image
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Reviews
Keith Price
This paper is at the boundary of computer graphics and computer vision. As such, it addresses a problem where the goal is to generate a scene that is both real (the computer vision problem) and attractive-looking (the computer graphics problem). The goal of the work is to recover the complete reflectance information for the surfaces in a scene, using a single image, in order to use graphics techniques to manipulate the final result. This will make it possible to add objects and change the viewpoint or lighting. The paper gives a good review of the various approaches that have been used to solve parts of this problem. The authors detail the techniques they have used to generate a more complete solution and provide one example. Their solution iteratively compares its prediction with the actual image and applies a more complex model to the regions where the differences are too large. This paper presents an important contribution in both computer vision and computer graphics. Unfortunately the space limitations constrained the size of most of the figures, which makes some of them difficult to read. Online Computing Reviews Service
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