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

Boxin Shi, Zhe Wu, Zhipeng Mo, Dinglong Duan, Sai-Kit Yeung, Ping Tan; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3707-3716

Abstract

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.

Related Material

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[bibtex]
@InProceedings{Shi_2016_CVPR,
author = {Shi, Boxin and Wu, Zhe and Mo, Zhipeng and Duan, Dinglong and Yeung, Sai-Kit and Tan, Ping},
title = {A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}