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Diffuse-Specular Separation using Binary Spherical GradientIllumination

Christos Kampouris; Stefanos Zafeiriou; Abhijeet Ghosh

Imperial College London

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Abstract

We introduce a novel method for view-independent diffuse-specular separation of albedo and photometric normals without requiring polarization using binary spherical gradient illumination. The key idea is that with binary gradient illumination, a dielectric surface oriented towards the dark hemisphere exhibits pure diffuse reflectance while a surface oriented towards the bright hemisphere exhibits both diffuse and specular reflectance. We exploit this observation to formulate diffuse-specular separation based on color-space analysis of a surface’s response to binary spherical gradients and their complements. The method does not impose restrictions on viewpoints and requires fewer photographs for multiview acquisition than polarized spherical gradient illumination. We further demonstrate an efficient two-shot capture using spectral multiplexing of the illumination that enables diffuse-specular separation of albedo and heuristic separation of photometric normals.

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Photometric acquisition; Facial capture; Frequency domain separation; Binary spherical gradients

Extracting the Shape and Roughness of Specular Lobe Objects using Four Light Photometric Stereo

Fredric Solomon, Katsushi Ikeuchi

Carnegie Mellon University

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Abstract

Two important aspects of part inspection are the measurement of surface shape and surface roughness. We propose a noncontact method of measuing surface shape and surface roughness. The method, which we call \"four light photometric stereo\" , uses four lights which sequentially illuminate the object under inspection, and a video camera for taking images of the object. Conceptually, the problem we are solving has three parts: shape extraction, pixel segmentation and roughness extraction. The shape information is produced directly by the three light and four light photometric stereo methods. After we have shape information, we can apply statistical segmentation techniques to determine which pixels are specular and which re non-specular. Then, we can use the specular pixels and shape information, in conjugation with a simplified Torrance-Sparrow reflectance model to determine the surface roughness The method has successfully been applied to a number of synthetic and real objects.

Circularly Polarized Spherical Illumination Reflectometry

Abhijeet Ghosh, Tongbo Chen, Pieter Peers, Cyrus A. Wilson, Paul Debevec

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We present a novel method for surface reflectometry from a few observations of a scene under a single uniform spherical field of circularly polarized illumination. The method is based on a novel analysis of the Stokes reflectance field of circularly polarized spherical illumination and yields per-pixel estimates of diffuse albedo, specular albedo, index of refraction, and specular roughness of isotropic BRDFs. To infer these reflectance parameters, we measure the Stokes parameters of the reflected light at each pixel by taking four photographs of the scene, consisting of three photographs with differently oriented linear polarizers in front of the camera, and one additional photograph with a circular polarizer. The method only assumes knowledge of surface orientation, for which we make a few additional photometric measurements. We verify our method with three different lighting setups, ranging from specialized to off-theshelf hardware, which project either discrete or continuous fields of spherical illumination. Our technique offers several benefits: it estimates a more detailed model of per-pixel surface reflectance parameters than previous work, it requires a relatively small number of measurements, it is applicable to a wide range of material types, and it is completely viewpoint independent.

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Reflectance Measurement and Model Fitting; Polarization-Based Reflectance Measurement; Reflectance Component Separation; Material Classification; Normal Estimation; Index of Refraction

An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale

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Category: CS

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