Exploiting the generic view assumption to estimate scene parameters

The generic view assumption states that an observer is not in a special position relative to the scene. The author shows how to use the generic view assumption to quantify the likelihood of a view and so derive a powerful tool to estimate scene parameters. Generic variables can include viewpoint, object orientation, and lighting position. By considering the image as a function of these variables, the probability that a set of scene parameters created a given image is derived. This scene probability equation includes a term which rewards fidelity to the image, and a generic view term, which favors scenes likely to produce the observed image. Large derivatives of the image with respect to the generic variables correspond to unlikely scenes. This approach reduces the dependence on prior assumptions and should increase the scope and accuracy of scene estimates. This framework applies to many vision problems. Several shape from shading examples are shown, including the estimation of reflectance function, light direction, and vertical scale, in cases where these are otherwise unknown