Surface Height Map Estimation From a Single Image Using Convolutional Neural Networks

ZHOU Xiaowei; ZHONG Guoqiang; QI Lin; DONG Junyu; MAO Jianzhou

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Surface Height Map Estimation From a Single Image Using Convolutional Neural Networks

ZHOU Xiaowei 1, ZHONG Guoqiang 1, QI Lin 1, DONG Junyu 1, MAO Jianzhou 2

1. Department of Computer Science and Technology, Ocean University of China, Qingdao 266100

2. Macau University of Science and Technology, Macau 999078

*Correspondence author

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Funding: Specialized Research Fund for the Doctoral Program of Higher Education （No.No.20120132110018）, National Natural Science Foundation of China （No.No.61271405）

Opened online:20 May 2016

Accepted by: none

Citation: ZHOU Xiaowei, ZHONG Guoqiang, QI Lin.Surface Height Map Estimation From a Single Image Using Convolutional Neural Networks[OL]. [20 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688898

Surface height map estimation is an important task in high-resolution 3D reconstruction. This task differs from general scene depth estimation in the fact that surface height maps contain more high frequency information or fine details. Existing methods based on radar, laser or other equipments can be used for large-scale scene depth recovery, but might fail in small-scale surface height map estimation. Although some methods are available for surface height reconstruction based on multiple images, e.g. photometric stereo, height map estimation directly from a single image is still a challenging issue. In this paper, we present a novel method based on convolutional neural networks for estimating the height map from a single texture image, without using any other equipments or extra prior knowledge of the image contents. Experimental results based on procedural and real texture datasets show that the proposed algorithm is effective and reliable.

Keywords:Image processing; Convolutional neural network; Texture; 3D reconstruction

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