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Specularity prediction is essential to many computer vision applications,giving important visual cues usable in Augmented Reality (AR), SimultaneousLocalisation and Mapping (SLAM), 3D reconstruction and material modeling.However, it is a challenging task requiring numerous information from the sceneincluding the camera pose, the geometry of the scene, the light sources and thematerial properties. Our previous work addressed this task by creating anexplicit model using an ellipsoid whose projection fits the specularity imagecontours for a given camera pose. These ellipsoid-based approaches belong to afamily of models called JOint-LIght MAterial Specularity (JOLIMAS), which wehave gradually improved by removing assumptions on the scene geometry. However,our most recent approach is still limited to uniformly curved surfaces. Thispaper generalises JOLIMAS to any surface geometry while improving the qualityof specularity prediction, without sacrificing computation performances. Theproposed method establishes a link between surface curvature and specularityshape in order to lift the geometric assumptions made in previous work.Contrary to previous work, our new model is built from a physics-based localillumination model namely Torrance-Sparrow, providing an improvedreconstruction. Specularity prediction using our new model is tested againstthe most recent JOLIMAS version on both synthetic and real sequences withobjects of various general shapes. Our method outperforms previous approachesin specularity prediction, including the real-time setup, as shown in thesupplementary videos.

A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces