2020/01/18 by Min Li, Zhenglong Zhou, Li, Min +9 · 8 citations
Computer Science · Mathematics · #Advanced Vision and Imaging #Artificial intelligence #Benchmark (surveying) #Bidirectional reflectance distribution function #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #Computer graphics (images) #Computer science #Computer vision #FOS: Computer and information sciences #Geology #Geometry #Image (mathematics) #Isotropy #Mathematics #Mean squared error #Optical measurement and interference techniques #Optics #Photometric stereo #Physics #Point (geometry) #Reflectivity #Set (abstract data type) #cs.CV
paper · pdf · doi:10.48550/arxiv.2001.06659
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/01/18 · openalex publication_date 2020/01/18 · arxiv updated 2020/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.