IIEEJ Transactions on Image Electronics and Visual Computing
Online ISSN : 2188-1901
Print ISSN : 2188-1898
ISSN-L : 2188-191X
Robust Sphere Detection in Unorganized 3D Point Clouds Using an Efficient Hough Voting Scheme Based on Sliding Voxels
Jaime SANDOVALKazuma UENISHIMunetoshi IWAKIRIKiyoshi TANAKA
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2020 Volume 8 Issue 2 Pages 121-135

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Abstract

Sphere detection in point clouds is an important task in 3D computer vision with various applications such as reverse engineering, medical imaging, Terrestrial Laser Scans (TLS) alignment, and so on. So far, several approaches have been proposed to detect spheres in point clouds. However, conventional methods are inefficient and inaccurate because they depend on random sampling, point-wise voting or normal vectors estimation to generate hypothetical spheres. To overcome these drawbacks, we propose a novel algorithm that employs sliding voxels and Hough voting to robustly and efficiently detect spheres in unorganized point clouds. The proposed method can analyze all the points contained in point clouds without deteriorating its efficiency and accuracy in contrast to conventional methods. Through experiments, we found that the proposed method can drastically reduce the processing time and achieve more accurate and robust performance in severer conditions than conventional methods.

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© 2020 The Institute of Image Electronics Engineers of Japan
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