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-the-shelf 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.

We present a novel technique for acquiring detailed facial geometry of a dynamic performance using extended spherical gradient illumination. Key to our method is a new algorithm for jointly aligning two photographs, under a gradient illumination condition and its complement, to a full-on tracking frame, providing dense temporal correspondences under changing lighting conditions. We employ a two-step algorithm to reconstruct detailed geometry for every captured frame. In the first step, we coalesce information from the gradient illumination frames to the full-on tracking frame, and form a temporally aligned photometric normal map, which is subsequently combined with dense stereo correspondences yielding a detailed geometry. In a second step, we propagate the detailed geometry back to every captured instance guided by the previously computed dense correspondences. We demonstrate reconstructed dynamic facial geometry, captured using moderate to video rates of acquisition, for every captured frame.

Abstract This paper presents a novel method for estimating specular roughness and tangent vectors, per surface point, from polarized second order spherical gradient illumination patterns. We demonstrate that for isotropic BRDFs, only three second order spherical gradients are sufficient to robustly estimate spatially varying specular roughness. For anisotropic BRDFs, an additional two measurements yield specular roughness and tangent vectors per surface point. We verify our approach with different illumination configurations which project both discrete and continuous fields of gradient illumination. Our technique provides a direct estimate of the per‐pixel specular roughness and thus does not require off‐line numerical optimization that is typical for the measure‐and‐fit approach to classical BRDF modeling.

Glare arises due to multiple scattering of light inside the camera's body and lens optics and reduces image contrast. While previous approaches have analyzed glare in 2D image space, we show that glare is inherently a 4D ray-space phenomenon. By statistically analyzing the ray-space inside a camera, we can classify and remove glare artifacts. In ray-space, glare behaves as high frequency noise and can be reduced by outlier rejection. While such analysis can be performed by capturing the light field inside the camera, it results in the loss of spatial resolution. Unlike light field cameras, we do not need to reversibly encode the spatial structure of the ray-space, leading to simpler designs. We explore masks for uniform and non-uniform ray sampling and show a practical solution to analyze the 4D statistics without significantly compromising image resolution. Although diffuse scattering of the lens introduces 4D low-frequency glare, we can produce useful solutions in a variety of common scenarios. Our approach handles photography looking into the sun and photos taken without a hood, removes the effect of lens smudges and reduces loss of contrast due to camera body reflections. We show various applications in contrast enhancement and glare manipulation.

We present a noniterative image cross-correlation approach to track translation and rotation of crawling cells in time-lapse video microscopy sequences. The method does not rely on extracting features or moments, and therefore does not impose specific requirements on the type of microscopy used for imaging. Here we use phase-contrast images. We calculate cell rotation and translation from one image to the next in two stages. First, rotation is calculated by cross correlating the images' polar-transformed magnitude spectra (Fourier magnitudes). Rotation of the cell about any center in the original images results in translation in this representation. Then, we rotate the first image such that the cell has the same orientation in both images, and cross correlate this image with the second image to calculate translation. By calculating the rotation and translation over each interval in the movie, and thereby tracking the cell's position and orientation in each image, we can then map from the stationary reference frame in which the cell was observed to the cell's moving coordinate system. We describe our modifications enabling application to nonidentical images from video sequences of moving cells, and compare this method's performance with that of a feature extraction method and an iterative optimization method.