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Abstract Color correction for photographed images is an ill‐posed problem. It is also a crucial initial step towards material acquisition for inverse rendering methods or pipelines. Several state‐of‐the‐art methods rely on reducing color differences for imaged reference color chart blocks of known color values to devise or optimize their solution. In this paper, we first establish through simulations the limitation of this minimality criteria which in principle results in overfitting. Next, we study and propose a few spatial distribution measures to augment the evaluation criteria. Thereafter, we propose a novel patch‐based, white‐point centric approach that processes luminance and chrominance information separately to improve on the color matching task. We compare our method qualitatively with several state‐of‐the art methods using our augmented evaluation criteria along with quantitative examinations. Finally, we perform rigorous experiments and demonstrate results to clearly establish the benefits of our proposed method. 

Inverse rendering pipelines are gaining prominence in realizing photo-realistic reconstruction of real-world objects for emulating them in virtual reality scenes. Apart from material reflectances, spectral rendering and in-scene illuminants' spectral power distributions (SPDs) play important roles in producing photo-realistic images. We present a simple, low-cost technique to capture and reconstruct the SPD of uniform illuminants. Instead of requiring a costly spectrometer for such measurements, our method uses a diffractive compact disk (CD-ROM) and a machine learning approach for accurate estimation. We show our method to work well with spotlights under simulations and few real-world examples. Presented results clearly demonstrate the reliability of our approach through quantitative and qualitative evaluations, especially in spectral rendering of iridescent materials. 

We introduce VarIS, our Variable Illumination Sphere – a multi-purpose system for acquiring and processing real-world geometric and appearance data for computer-graphics research and production. Its key applications among many are (1) human-face capture, (2) model scanning, and (3) spatially varying material acquisition. Facial capture requires high-resolution cameras at multiple viewpoints, photometric capabilities, and a swift process due to human movement. Acquiring a digital version of a physical model is somewhat similar but with different constraints for image processing and more allowable time. Each requires detailed estimations of geometry and physically based shading properties. Measuring spatially varying light-scattering properties requires spanning four dimensions of illumination and viewpoint with angular, spatial, and spectral accuracy, and this process can also be assisted using multiple, simultaneous viewpoints or rapid switching of lights with no movement necessary. VarIS is a system of hardware and software for spherical illumination and imaging that has been custom designed and developed by our team. It has been inspired by Light Stages and goniophotometers, but costs less through use of primarily off-the-shelf components, and additionally extends capabilities beyond these devices. In this paper we describe the unique system and contributions, including practical details that could assist other researchers and practitioners.