Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence

2016 ◽  
Author(s):  
Jin Xie ◽  
Meng Wang ◽  
Yi Fang
Keyword(s):  
Shape Descriptor ◽  
Spectral Shape ◽  
Shape Correspondence ◽  
3D Shape

scholarly journals 3D Shape Descriptor for Segmenting Point Cloud Data

2012 ◽  
Vol 30 (6_2) ◽  
pp. 643-651 ◽  
Author(s):  
So Young Park ◽  
Eun Jin Yoo ◽  
Dong-Cheon Lee ◽  
Yong Wook Lee
Keyword(s):  
Point Cloud ◽  
Shape Descriptor ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Shape

scholarly journals A 3D shape descriptor based on spectral analysis of medial axis

2015 ◽  
Vol 39 ◽  
pp. 50-66 ◽  
Author(s):  
Shuiqing He ◽  
Yi-King Choi ◽  
Yanwen Guo ◽  
Xiaohu Guo ◽  
Wenping Wang
Keyword(s):  
Spectral Analysis ◽  
Medial Axis ◽  
Shape Descriptor ◽  
3D Shape

scholarly journals Shape Correspondence Analysis for Biomolecules Based on Volumetric Eigenfunctions

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Tao Liao ◽  
Hao-Chih Lee ◽  
Ge Yang ◽  
Yongjie Jessica Zhang
Keyword(s):  
Correspondence Analysis ◽  
Flexible Structures ◽  
Spectral Shape ◽  
The Other ◽  
Shape Deformation ◽  
Second Step ◽  
Analysis Method ◽  
Shape Correspondence ◽  
Dense Point ◽  
Different Shapes

AbstractThe functionality of biomolecules depends on their flexible structures, which can be characterized by their surface shapes. Tracking the deformation and comparing biomolecular shapes are essential in understanding their mechanisms. In this paper, a new spectral shape correspondence analysis method is introduced for biomolecules based on volumetric eigenfunctions. The eigenfunctions are computed from the joint graph of two given shapes, avoiding the sign flipping and confusion in the order of modes. An initial correspondence is built based on the distribution of a shape diameter, which matches similar surface features in different shapes and guides the eigenfunction computation. A two-step scheme is developed to determine the final correspondence. The first step utilizes volumetric eigenfunctions to correct the assignment of boundary nodes that disobeys the main structures. The second step minimizes the distortion induced by deforming one shape to the other. As a result, a dense point correspondence is constructed between the two given shapes, based on which we approximate and predict the shape deformation, as well as quantitatively measure the detailed shape differences.

A new sizing system based on 3D shape descriptor for morphology clustering

2017 ◽  
Vol 113 ◽  
pp. 683-692 ◽  
Author(s):  
Moez Hamad ◽  
Sébastien Thomassey ◽  
Pascal Bruniaux
Keyword(s):  
Shape Descriptor ◽  
3D Shape ◽  
Sizing System

Learning a discriminative deformation-invariant 3D shape descriptor via many-to-one encoder

2016 ◽  
Vol 83 ◽  
pp. 330-338 ◽  
Author(s):  
Guoxian Dai ◽  
Jin Xie ◽  
Fan Zhu ◽  
Yi Fang
Keyword(s):  
Shape Descriptor ◽  
3D Shape

3D shape descriptor for object recognition based on Kinect-like depth image

2014 ◽  
Vol 32 (4) ◽  
pp. 260-269 ◽  
Author(s):  
M.A. As'ari ◽  
U.U. Sheikh ◽  
E. Supriyanto
Keyword(s):  
Object Recognition ◽  
Shape Descriptor ◽  
Depth Image ◽  
3D Shape

scholarly journals Deformation-driven topology-varying 3D shape correspondence

2015 ◽  
Vol 34 (6) ◽  
pp. 1-13 ◽  
Author(s):  
Ibraheem Alhashim ◽  
Kai Xu ◽  
Yixin Zhuang ◽  
Junjie Cao ◽  
Patricio Simari ◽  
...  
Keyword(s):  
Shape Correspondence ◽  
3D Shape
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