Improved spin images for 3D surface matching using signed angles

Despite the popularity of spin images in surface matching and registration, disadvantages such as noise sensitivity and low discriminative ability still hindered their usefulness in real applications. In this paper, a novel approach was proposed for improving the spin images. The proposed method mod...

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Main Authors: ZHANG, Zhiyuan, ONG, Sim Heng, FOONG, Kelvin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/7946
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spelling sg-smu-ink.sis_research-89492023-07-20T03:18:05Z Improved spin images for 3D surface matching using signed angles ZHANG, Zhiyuan ONG, Sim Heng FOONG, Kelvin Despite the popularity of spin images in surface matching and registration, disadvantages such as noise sensitivity and low discriminative ability still hindered their usefulness in real applications. In this paper, a novel approach was proposed for improving the spin images. The proposed method modified the standard spin images by using angle information between the normals of reference point and neighboring points. This information largely increased the robustness to noise without losing the intrinsic advantages of spin images. Moreover, signs were defined to incorporate the directions of angles which were shown to be able to further improve the descriptive power. Experiments were also conducted to show the outperformance of improved spin images under different levels of noise, and good agreements were obtained by comparing with the standard spin images and a recent popular 3D descriptor. 2012-10-03T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/7946 info:doi/10.1109/icip.2012.6466915 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Silicon Noise Shape Standards Clutter Histograms Robustness Artificial Intelligence and Robotics Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Silicon
Noise
Shape
Standards
Clutter
Histograms
Robustness
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
spellingShingle Silicon
Noise
Shape
Standards
Clutter
Histograms
Robustness
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
ZHANG, Zhiyuan
ONG, Sim Heng
FOONG, Kelvin
Improved spin images for 3D surface matching using signed angles
description Despite the popularity of spin images in surface matching and registration, disadvantages such as noise sensitivity and low discriminative ability still hindered their usefulness in real applications. In this paper, a novel approach was proposed for improving the spin images. The proposed method modified the standard spin images by using angle information between the normals of reference point and neighboring points. This information largely increased the robustness to noise without losing the intrinsic advantages of spin images. Moreover, signs were defined to incorporate the directions of angles which were shown to be able to further improve the descriptive power. Experiments were also conducted to show the outperformance of improved spin images under different levels of noise, and good agreements were obtained by comparing with the standard spin images and a recent popular 3D descriptor.
format text
author ZHANG, Zhiyuan
ONG, Sim Heng
FOONG, Kelvin
author_facet ZHANG, Zhiyuan
ONG, Sim Heng
FOONG, Kelvin
author_sort ZHANG, Zhiyuan
title Improved spin images for 3D surface matching using signed angles
title_short Improved spin images for 3D surface matching using signed angles
title_full Improved spin images for 3D surface matching using signed angles
title_fullStr Improved spin images for 3D surface matching using signed angles
title_full_unstemmed Improved spin images for 3D surface matching using signed angles
title_sort improved spin images for 3d surface matching using signed angles
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/7946
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