Detail-feature-preserving surface reconstruction

Authors:
Xu Zhao;Zhong Zhou;Ye Duan;Wei Wu
Affiliations:
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China;State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China;Computer Graphics and Image Understanding Lab, Department of Computer Science, University of Missouri – Columbia, Columbia, MOUSA;State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China
Venue:
Computer Animation and Virtual Worlds
Year:
2012

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Abstract

In this paper, we propose a feature-preserving surface reconstruction method from sparse noisy 3D measurements such as range scanning or passive multiview stereo. In contrast to earlier methods, we define a novel type of explicit 3D filter—regularized weighted least squares filter—to characterize the detail features such as surface wrinkles and sharp features. To account for noise, we rasterize input-oriented points into a probabilistic volume (base volume) and then create a guidance volume by Gaussian filtering. Both the base volume and the guidance volume are further filtered by regularized weighted least squares filter to detect and recover detail features. After the two-stage filtering, a global minimal surface is computed by graph cut and meshed as a geometric model. Experimental results on various datasets show that our method is robust to noise, outliers, and missing parts, which makes it more suitable to fit indoor/outdoor multiview stereo data. Unlike other methods, our method can completely recover scene structures and preserve detail features from noisy point samples. Copyright © 2012 John Wiley & Sons, Ltd.