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We present a method to capture both 3D shape and spatially varyingreflectance with a multi-view photometric stereo (MVPS) technique that worksfor general isotropic materials. Our algorithm is suitable for perspectivecameras and nearby point light sources. Our data capture setup is simple, whichconsists of only a digital camera, some LED lights, and an optional automaticturntable. From a single viewpoint, we use a set of photometric stereo imagesto identify surface points with the same distance to the camera. We collectthis information from multiple viewpoints and combine it withstructure-from-motion to obtain a precise reconstruction of the complete 3Dshape. The spatially varying isotropic bidirectional reflectance distributionfunction (BRDF) is captured by simultaneously inferring a set of basis BRDFsand their mixing weights at each surface point. In experiments, we demonstrateour algorithm with two different setups: a studio setup for highest precisionand a desktop setup for best usability. According to our experiments, under thestudio setting, the captured shapes are accurate to 0.5 millimeters and thecaptured reflectance has a relative root-mean-square error (RMSE) of 9%. Wealso quantitatively evaluate state-of-the-art MVPS on a newly collectedbenchmark dataset, which is publicly available for inspiring future research.

Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials