Fast Lighting/Rendering Solution for Matching a 2D Image to a Database of 3D Models: “Lightsphere”

Résumé

We describe a method for object recognition with 2D image queries to be identified from among a set of 3D models. The pose is known from a previous step. The main target application is face recognition. The 3D models consist of both shape and color texture information, and the 2D queries are color camera images. The kernel of the method consists of a lookup table that associates 3D surface normals with expected image brightness, modulo albedo, for a given query. This lookup table is fast to compute, and is used to render images from the models for a sum of square difference error measure. Using a data set of 42 face models and 1764 (high quality) query images under 7 poses and 6 lighting conditions, we achieve average recognition accuracy of about 83%, with more than 90% in several pose/lighting conditions, using semi-automatically computed poses. The method is extremely fast compared to those that involve finding eigenvectors or solving constrained equation systems.

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