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research-article

V4PCS: Volumetric 4PCS Algorithm for Global Registration

[+] Author and Article Information
Jida Huang

Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260
jidahuan@buffalo.edu

Tsz Ho Kwok

Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, Canada H3G 1M8
tszho.kwok@concordia.ca

chi zhou

Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260
chizhou@buffalo.edu

1Corresponding author.

ASME doi:10.1115/1.4037477 History: Received February 17, 2017; Revised June 12, 2017

Abstract

With the advances in three-dimensional (3D) scanning and sensing technologies, massive human-related data are now available and create many applications in data-driven design. Similarity identification is one of the basic problems in data-driven design and can facilitate many engineering applications and product paradigm such as quality control and mass customization. Therefore, reusing information can create unprecedented opportunities in advancing the theory, method, and practice of product design. To enable information reuse, different models have to be aligned so that their similarity can be identified. This alignment is commonly known as the global registration that finds an optimal rigid transformation to align two 3D shapes (scene and model) without any assumptions on their initial positions. The Super 4-Points Congruent Sets (S4PCS) is a popular algorithm used for this shape registration. While S4PCS performs the registration using a set of 4 coplanar points, we find that incorporating the volumetric information of the models can improve the robustness and the efficiency of the algorithm, which are particularly important for mass customization. In this paper, we propose a novel algorithm, Volumetric 4PCS (V4PCS), to extend the 4 coplanar points to non-coplanar ones for global registration, and theoretically demonstrate the computational complexity is significantly reduced. Experimental tests are conducted on a number of models to compare with S4PCS. The experimental results show that the proposed V4PCS can achieve a maximum of 20 times speedup and can successfully compute the valid transformation with very limited number of sample points.

Copyright (c) 2017 by ASME
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