Conference Paper/Proceeding/Abstract 815 views 79 downloads
Shape Correspondence with Isometric and Non-Isometric Deformations
R. M. Dyke,
C Stride,
Y.-K. Lai,
P. L. Rosin,
M. Aubry,
A. Boyarski,
A. M. Bronstein,
M. M. Bronstein,
D. Cremers,
M. Fisher,
T. Groueix,
D. Guo,
V. G. Kim,
R. Kimmel,
Z. Lähner,
K. Li,
O. Litany,
T. Remez,
E. Rodolà,
B. C. Russell,
Y. Sahillioglu,
R. Slossberg,
M. Vestner,
Z. Wu,
J. Yang,
Gary Tam 
Eurographics Workshop on 3D Object Retrieval
Swansea University Author:
Gary Tam
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DOI (Published version): 10.2312/3dor.20191069
Abstract
The registration of surfaces with non-rigid deformation, especially non-isometric deformations, is a challenging problem. When applying such techniques to real scans, the problem is compounded by topological and geometric inconsistencies between shapes. In this paper, we capture a benchmark dataset...
| Published in: | Eurographics Workshop on 3D Object Retrieval |
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| ISBN: | 978-3-03868-077-2 |
| ISSN: | 1997-0471 |
| Published: |
The Eurographics Association
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa52380 |
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| Abstract: |
The registration of surfaces with non-rigid deformation, especially non-isometric deformations, is a challenging problem. When applying such techniques to real scans, the problem is compounded by topological and geometric inconsistencies between shapes. In this paper, we capture a benchmark dataset of scanned 3D shapes undergoing various controlled deformations (articulating, bending, stretching and topologically changing), along with ground truth correspondences. With the aid of thistiered benchmark of increasingly challenging real scans, we explore this problem and investigate how robust current stateof-the-art methods perform in different challenging registration andc orrespondence scenarios. We discover that changes in topology is a challenging problem for some methods and that machine learning-based approaches prove to be more capable of handling non-isometric deformations on shapes that are moderately similar to the training set. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
This work has
been supported by the Cardiff University EPSRC Doctoral Training Partnership [grant ref. EP/N509449/1], and by the Scientific
and Technological Research Council of Turkey (TÜB˙ITAK) [grant
ref. EEEAG-115E471]. |