A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo

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 dataset with calibrated Directional Lightings, objects of General reflectance, and 'ground Truth' shapes (normals). Based on our dataset, we quantitatively evaluate state-of-the-art photometric stereo methods for general non-Lambertian materials and unknown lightings to analyze their strengths and limitations. read more

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here