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A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
2018
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions. However, due to the lack of suitable benchmark data with ground truth shapes (normals), quantitative comparison and evaluation is difficult to achieve. In this paper, we first survey and categorize existing methods using a photometric stereo taxonomy emphasizing on non-Lambertian and uncalibrated methods. We then introduce the 'DiLiGenT' photometric stereo image
doi:10.1109/tpami.2018.2799222
pmid:29993473
fatcat:oro4tiz5iffnre6xvlfcpmrjsq