A Geometric Model for Specularity Prediction on Planar Surfaces with Multiple Light Sources

Alexandre Morgand, mohamed Tamaazousti, Adrien Bartoli

Abstract:

Specularities are often problematic in computer vision since they impact the dynamic range of the image intensity. A natural
approach would be to predict and discard them using computer graphics models. However, these models depend on parameters which
are difficult to estimate (light sources, objects’ material properties and camera). We present a geometric model called JOLIMAS: JOint
LIght-MAterial Specularity, which predicts the shape of specularities. JOLIMAS is reconstructed from images of specularities observed
on a planar surface. It implicitly includes light and material properties, which are intrinsic to specularities. This model was motivated by
the observation that specularities have a conic shape on planar surfaces. The conic shape is obtained by projecting a fixed quadric on
the planar surface. JOLIMAS thus predicts the specularity using a simple geometric approach with static parameters (object material
and light source shape). It is adapted to indoor light sources such as light bulbs and fluorescent lamps. The prediction has been tested
on synthetic and real sequences. It works in a multi-light context by reconstructing a quadric for each light source with special cases
such as lights being switched on or off. We also used specularity prediction for dynamic retexturing and obtained convincing rendering
results. Further results are presented as supplementary video material.