An Empirical Model for Specularity Prediction with Application to Dynamic Retexturing

Abstract

Specularities, which are often visible in images, may be problematic in computer vision since they depend on parameters which are difficult to estimate in practice. We present an empirical model called JOLIMAS: JOint LIght-MAterial Specularity, which allows specularity prediction. JOLIMAS is reconstructed from images of specular reflections observed on a planar surface and implicitly includes light and material properties which are intrinsic to specularities. This work was motivated by the observation that specularities have a conic shape on planar surfaces. A theoretical study on the well known illumination models of Phong and Blinn-Phong was conducted to support the accuracy of this hypothesis. A conic shape is obtained by projecting a quadric on a planar surface. We showed empirically the existence of a fixed quadric whose perspective projection fits the conic shaped specularity in the associated image. JOLIMAS predicts the complex phenomenon of specularity using a simple geometric approach with static parameters on the object material and on the light source shape. It is adapted to indoor light sources such as light bulbs or fluorescent lamps. The performance of the prediction was convincing on synthetic and real sequences. Additionally, we used the specularity prediction for dynamic retexturing and obtained convincing rendering results. Further results are presented as supplementary material.