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Optimizing human model reconstruction from RGB-D images based on skin detection

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Abstract

This paper reconstructs human model from multi-view RGB-D images of an Xbox One Kinect. We preprocess the depth images by implicit surface de-noising and then part-wisely register them into a point cloud. A template model is selected from the human model database to fit the registered point cloud of a human body by Laplacian deformation. Skin detection of RGB-D images helps to tightly constrain the skin parts of human body in template fitting step in order to get more precise and lifelike human model. We propose a robust skin detection method that is not affected by clothing pattern and background. Experiments demonstrate the effectiveness of our method.

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Acknowledgments

This work was partially supported by National Natural Science Foundation of China (51575481, 61379096) and Project of Public Technology Research in Industry of Zhejiang Province (2014C31048).

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Authors and Affiliations

  1. Research Center of Design and Production Innovation, College of Mechanical Engineering, Zhejiang University, Room 416, No. 1 Teaching Building, Zhejiang University Yuquan Campus, No. 38 Zheda Road, Hangzhou, 310027, People’s Republic of China

    Guang Chen, Jituo Li, Jiping Zeng, Bei Wang & Guodong Lu

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  1. Guang Chen
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  2. Jituo Li
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  3. Jiping Zeng
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  4. Bei Wang
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  5. Guodong Lu
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Correspondence to Jituo Li.

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Chen, G., Li, J., Zeng, J. et al. Optimizing human model reconstruction from RGB-D images based on skin detection. Virtual Reality 20, 159–172 (2016). https://doi.org/10.1007/s10055-016-0291-y

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  • DOI: https://doi.org/10.1007/s10055-016-0291-y

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