Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88038
Title: Fast and robust shape diameter function
Authors: Chen, Shuangmin
Liu, Taijun
Shu, Zhenyu
Xin, Shiqing
He, Ying
Tu, Changhe
Keywords: Noise
Algorithm
Issue Date: 2018
Source: Chen, S., Liu, T., Shu, Z., Xin, S., He, Y., & Tu, C. (2018). Fast and robust shape diameter function. PLOS ONE, 13(1), e0190666-.
Series/Report no.: PLOS ONE
Abstract: The shape diameter function (SDF) is a scalar function defined on a closed manifold surface, measuring the neighborhood diameter of the object at each point. Due to its pose oblivious property, SDF is widely used in shape analysis, segmentation and retrieval. However, computing SDF is computationally expensive since one has to place an inverted cone at each point and then average the penetration distances for a number of rays inside the cone. Furthermore, the shape diameters are highly sensitive to local geometric features as well as the normal vectors, hence diminishing their applications to real-world meshes which often contain rich geometric details and/or various types of defects, such as noise and gaps. In order to increase the robustness of SDF and promote it to a wide range of 3D models, we define SDF by offsetting the input object a little bit. This seemingly minor change brings three significant benefits: First, it allows us to compute SDF in a robust manner since the offset surface is able to give reliable normal vectors. Second, it runs many times faster since at each point we only need to compute the penetration distance along a single direction, rather than tens of directions. Third, our method does not require watertight surfaces as the input—it supports both point clouds and meshes with noise and gaps. Extensive experimental results show that the offset-surface based SDF is robust to noise and insensitive to geometric details, and it also runs about 10 times faster than the existing method. We also exhibit its usefulness using two typical applications including shape retrieval and shape segmentation, and observe a significant improvement over the existing SDF.
URI: https://hdl.handle.net/10356/88038
http://hdl.handle.net/10220/44519
DOI: http://dx.doi.org/10.1371/journal.pone.0190666
Rights: © 2018 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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