PS-FCN: A Flexible Learning Framework for Photometric Stereo

Guanying Chen, Kai Han, Kwan-Yee K. Wong; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3-18

Abstract


This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo. Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.

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[bibtex]
@InProceedings{Chen_2018_ECCV,
author = {Chen, Guanying and Han, Kai and Wong, Kwan-Yee K.},
title = {PS-FCN: A Flexible Learning Framework for Photometric Stereo},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}