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A 3D model perceptual feature metric based on global height field

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Abstract

Human visual attention system tends to be attracted to perceptual feature points on 3D model surfaces. However, purely geometric-based feature metrics may be insufficient to extract perceptual features, because they tend to detect local structure details. Intuitively, the perceptual importance degree of vertex is associated with the height of its geometry position between original model and a datum plane. So, we propose a novel and straightforward method to extract perceptually important points based on global height field. Firstly, we construct spectral domain using Laplace–Beltrami operator, and we perform spectral synthesis to reconstruct a rough approximation of the original model by adopting low-frequency coefficients, and make it as the 3D datum plane. Then, to build global height field, we calculate the Euclidean distance between vertex geometry position on original surface and the one on 3D datum plane. Finally, we set a threshold to extract perceptual feature vertices. We implement our technique on several 3D mesh models and compare our algorithm to six state-of-the-art interest points detection approaches. Experimental results demonstrate that our algorithm can accurately capture perceptually important points on arbitrary topology 3D model.

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Acknowledgments

This research is supported by Natural Science Foundation of China (NSFC) (Nos. 61232011, 61320106008), NSFC-Guangdong Joint Fund (No. U1135003), National Natural Science Foundation of China (No. 61502541). The 3D models are courtesy of Stanford University, Princeton University and the Aim@Shape.

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Authors and Affiliations

  1. National Engineering Research Center of Digital Life, School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Yihui Guo, Shujin Lin, Zhuo Su, Xiaonan Luo, Ruomei Wang & Yang Kang

  2. Department of Internet Finance and Information Technology, Guangdong University of Finance, Guangzhou, 510521, China

    Yihui Guo

  3. School of Communication and Design, Sun Yat-sen University, Guangzhou, 510006, China

    Shujin Lin

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  1. Yihui Guo
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  2. Shujin Lin
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  3. Zhuo Su
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  4. Xiaonan Luo
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Correspondence to Shujin Lin.

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Guo, Y., Lin, S., Su, Z. et al. A 3D model perceptual feature metric based on global height field. Vis Comput 32, 1151–1164 (2016). https://doi.org/10.1007/s00371-015-1199-3

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