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

TL;DR

This paper extends a specularity prediction model to general curved surfaces, improving accuracy and maintaining real-time performance by linking surface curvature with specularity shape using a physics-based illumination model.

Contribution

It generalizes the JOLIMAS model to arbitrary surface geometries and enhances prediction quality without increasing computational costs.

Findings

Outperforms previous models in synthetic and real sequences

Maintains real-time performance in specularity prediction

Provides more accurate specularity reconstructions

Abstract

Specularity prediction is essential to many computer vision applications, giving important visual cues usable in Augmented Reality (AR), Simultaneous Localisation and Mapping (SLAM), 3D reconstruction and material modeling. However, it is a challenging task requiring numerous information from the scene including the camera pose, the geometry of the scene, the light sources and the material properties. Our previous work addressed this task by creating an explicit model using an ellipsoid whose projection fits the specularity image contours for a given camera pose. These ellipsoid-based approaches belong to a family of models called JOint-LIght MAterial Specularity (JOLIMAS), which we have gradually improved by removing assumptions on the scene geometry. However, our most recent approach is still limited to uniformly curved surfaces. This paper generalises JOLIMAS to any surface geometry while improving the quality of specularity prediction, without sacrificing computation performances. The proposed method establishes a link between surface curvature and specularity shape in order to lift the geometric assumptions made in previous work. Contrary to previous work, our new model is built from a physics-based local illumination model namely Torrance-Sparrow, providing an improved reconstruction. Specularity prediction using our new model is tested against the most recent JOLIMAS version on both synthetic and real sequences with objects of various general shapes. Our method outperforms previous approaches in specularity prediction, including the real-time setup, as shown in the supplementary videos.