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Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Fully Automatic and Robust 3D Modeling for Range Scan Data of Complex 3D Objects

Authors : Jungjae Yim, Guoliang Fan

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

3D surface registration of two or more range scans is an important step in building a complete 3D model of an object. When the overlaps between multi-view scans are insufficient, good initial alignment is necessary that usually requires some prior assumption such as pre-defined initial camera configuration or the use of landmarks. Specifically, this paper addresses the problem of registering two or more range scans captured from complex 3D objects which have small overlaps. The proposed technique is based on the integration of a new Partial Artificial Heat Kernel Signature (PA-HKS) and a Modified Multi-view Iterative Contour Coherence (MM-ICC) algorithm. This unique combination allows us to handle multi-view range scan data with large out-of-plane rotation and with limited overlaps between every two adjacent views. The experimental results on several complex 3D objects show the effectiveness and robustness of the proposed approach.

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previous chapter Improved Stereo Vision of Indoor Dense Suspended Scatterers Scenes from De-scattering Images
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Metadata
Title
Fully Automatic and Robust 3D Modeling for Range Scan Data of Complex 3D Objects
Authors
Jungjae Yim
Guoliang Fan
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_48

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