LightSphere: Fast lighting compensation for matching a 2D image to a 3D model

We describe a fast object recognition method that identifies 2D color image queries among a set of 3D models. It is fast enough for searching a very large database. The main application is face recognition, for which we report very good accuracy over a wide range of pose and lighting conditions. We make weaker assumptions about both lighting and reflectance than are usual. We avoid finding eigen-vectors or solving systems of equations. Instead, we use the query to estimate a specialization of the BRDF to the fixed lighting and pose of the query. In a single image pass, we compute a lookup table for re-rendering, which represents expectation values for the action of the light via the BRDF This yields a similarity measure of the consistency between model and query under the regularity assumptions. We report recognition results on a data set of 42 3D face models and 1764 query images, comprising 7 poses and 6 lighting conditions. The recognition accuracy is indistinguishable from much slower methods, methods which make stronger assumptions about the BRDF and lighting.