This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.
Bibliographical noteFunding Information:
This research was funded by the Natural Science Foundation of Shandong Province of China (ZR2018ZB0852).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright 2021 Elsevier B.V., All rights reserved.
- And laser extraction
- Generative adversarial network
- Image in-painting
- Multi-spectral photometric stereo
ASJC Scopus subject areas
- Analytical Chemistry
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering