Sensors 2018, 18(3), 764; https://doi.org/10.3390/s18030764
Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
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These authors contributed equally to this work and should be considered co-first authors.
Received: 17 December 2017 / Revised: 14 February 2018 / Accepted: 14 February 2018 / Published: 2 March 2018
(This article belongs to the Special Issue Sensing, Data Analysis and Platforms for Ubiquitous Intelligence)
Abstract
Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN) instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods. View Full-Text
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Lu, L.; Qi, L.; Luo, Y.; Jiao, H.; Dong, J. Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo. Sensors 2018, 18, 764.
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