We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.
A self-training-based spectral reflectance recovery method was developed to accurately reconstruct the spectral images of art paintings with multispectral imaging. By partitioning the multispectral images with the k-means clustering algorithm, the training samples are directly extracted from the art painting itself to restrain the deterioration of spectral estimation caused by the material inconsistency between the training samples and the art painting. Coordinate paper is used to locate the extracted training samples. The spectral reflectances of the extracted training samples are acquired indirectly with a spectroradiometer, and the circle Hough transform is adopted to detect the circle measuring area of the spectroradiometer. Through simulation and a practical experiment, the implementation of the proposed method is explained in detail, and it is verified to have better reflectance recovery performance than that using the commercial target and is comparable to the approach using a painted color target.
Metamer mismatching is a phenomenon where two objects that are colorimetrically indistinguishable under one lighting condition become distinguishable under another one. Due to the unavailability of spectral information, metamer mismatching introduces an inherent uncertainty into cameras' color reproduction. To investigate the degree of image quality degradation by the metamer mismatching, a large spectral reflectance database was compiled in this study to search the object-color metamers sets of the spectra in hyperspectral images. Then, metamer-degraded images were constructed and compared with the ground truth images by directional statistics-based color similarity index image quality assessment metrics to evaluate the perceptual image degradation. The results indicate that the object-color metamer mismatching has only little impact on the image quality degradation, whereas the inappropriate selection of color correction matrices involved with the illumination metamerism is the primary factor for the accuracy decrease in the digital camera color reproduction.
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