We propose a novel, practical solution for high quality reconstruction of axially-symmetric transparent objects such as glasses, tumblers, goblets, carafes, etc., using single-view transmission imaging of a few patterns emitted from a background LCD panel. Our approach employs inverse ray tracing to reconstruct both completely symmetric as well as more complex n-fold symmetric everyday transparent objects.

We present two novel and complimentary approaches to measure diffraction effects in commonly found planar spatially varying holographic surfaces. Such holographic surfaces are usually manufactured with one dimensional diffraction gratings that are varying in periodicity and orientation over an entire sample in order to produce a wide range of diffraction effects such as gradients and kinematic (rotational) effects. Our proposed methods estimate these two parameters and allow an accurate reproduction of these effects in real-time.

We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. We couple polarization imaging with deep learning to achieve high quality estimate of 3D object shape (surface normals and depth) and SVBRDF using single-view polarization imaging under frontal flash illumination.

We present a deep neural network-based method that acquires high-quality shape and spatially varying reflectance of 3D
objects using smartphone multi-lens imaging. Our method acquires two images simultaneously using a zoom lens and a wide-angle lens of a smartphone under either natural illumination or phone flash conditions, effectively functioning like a single-shot method.

We present a novel desktop-based system for high-quality facial capture including geometry and facial appearance. The proposed acquisition system is highly practical and scalable, consisting purely of commodity components. The setup consists of a set of displays for controlled illumination for reflectance capture, in conjunction with multiview acquisition of facial geometry.

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 method does not impose restrictions on viewpoints and requires fewer photographs for multiview acquisition than polarized spherical
gradient illumination.

We propose an efficient method for reproducing diffraction colours on natural surfaces with complex nanostructures that can be represented as height-fields. Our method employs Chebyshev approximations to accurately model view-dependent iridescences for such a surface into its spectral bidirectional reflectance distribution function (BRDF). As main contribution, our method significantly reduces the runtime memory footprint from precomputed lookup tables without compromising photorealism.

We present a novel and practical approach for image-based relighting that employs the lights available in a regular room to acquire the reflectance field of an object. We achieve plausible results for diffuse and glossy objects that are qualitatively similar to results produced with dense sampling of the reflectance field including using a light stage. We believe our approach can be applied for practical relighting applications with general studio lighting.

We propose two novel setups for acquiring spatially varying surface reflectance properties of planar samples using mobile devices. Our first setup employs free-form handheld acquisition with the back camera-flash pair on a typical mobile device and is suitable for rough specular samples. Ours second setup, suitable for highly specular samples, employs the LCD panel on a tablet as an extended illumination source.

We have built a multispectral LED sphere (light stage) consisting of 168 RGB and color temperature controllable white LED lamps, respectively. The LED sphere has a 2.5 meter diameter steel structure, and employs off-the-shelf programmable LED lamps.