Authors:
Yulan Guo
1
;
Ferdous Sohel
2
;
Mohammed Bennamoun
2
;
Min Lu
3
and
Jianwei Wan
3
Affiliations:
1
National University of Defense Technology and The University of Western Australia, China
;
2
The University of Western Australia, Australia
;
3
National University of Defense Technology, China
Keyword(s):
Local Surface Descriptor, 3D Modeling, 3D Object Recognition, Range Image Registration.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Modeling and Algorithms
;
Pattern Recognition
;
Scene and Object Modeling
;
Software Engineering
;
Surface Modeling
Abstract:
Local surface description is a critical stage for surface matching. This paper presents a highly distinctive local
surface descriptor, namely TriSI. From a keypoint, we first construct a unique and repeatable local reference
frame (LRF) using all the points lying on the local surface. We then generate three spin images from the three
coordinate axes of the LRF. These spin images are concatenated and further compressed into a TriSI descriptor
using the principal component analysis technique. We tested our TriSI descriptor on the Bologna Dataset
and compared it to several existing methods. Experimental results show that TriSI outperformed existing
methods under all levels of noise and varying mesh resolutions. The TriSI was further tested to demonstrate
its effectiveness in 3D modeling. Experimental results show that it can accurately perform pairwise and
multiview range image registration. We finally used the TriSI descriptor for 3D object recognition. The results
on the UWA Dataset s
how that TriSI outperformed the state-of-the-art methods including spin image, tensor
and exponential map. The TriSI based method achieved a high recognition rate of 98.4%.
(More)