Color consistency of specular highlights in consumer cameras

Lin, John Y.; Tamaazousti, Mohamed; Said, Souheil Hadj; Morgand, Alexandre

doi:10.1145/3139131.3141216

Cited by 1 publication

(1 citation statement)

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“…In parallel, other works have been proposed on primary light source reconstruction. Ideally, each light source should be associated to a geometry (position and shape), a colour and an intensity value in order to match the scene [31], [33] realistically. Although numerous light source models exist in computer graphics, the models used in computer vision are generally divided in two categories: directional light sources and point light sources.…”

Section: Local Illumination Modelsmentioning

confidence: 99%

A multiple-view geometric model of specularities on non-uniformly curved surfaces

Morgand

Tamaazousti

Bartoli

2017

Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology

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Specularity prediction is essential to many computer vision applications. In Augmented Reality (AR), it improves the realism of virtual objects inserted in a live video stream by providing a coherent shape, appearance and motion to specularities. Specularity prediction also gives important visual cues that could be used in Simultaneous Localisation and Mapping (SLAM), 3D reconstruction and material modeling, thus improving scene understanding. However, specularity prediction 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 have 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), where we have attempted to gradually remove assumptions on the scene such as the geometry of the specular surfaces to provide real-time specularity prediction. However, our most recent approach is still limited to uniformly curved surfaces. This paper builds upon these methods by generalising 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 from previous work. Contrary to previous work, our new model is built from a physics-based local illumination model namely Torrance-Sparrow, providing a better model reconstruction. Specularity prediction using our new model is tested against the most recent JOLIMAS version on both synthetic and real sequences with objects of varying shape curvatures, reconstructed using a CAD model or the Kinect v2 depth camera. Our method outperforms previous approaches in specularity prediction, including the real-time setup, as shown in the supplementary material using videos.

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Section: Local Illumination Modelsmentioning

confidence: 99%