Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
SHREC 16 Matching of Deformable Shapes with Topological Noise
Date
2016-06-15
Author
Lahner, Zorah
Rodola, Emanuele
Bronstein, Michael
Cremers, Daniel
Burghard, Oliver
Cosmo, Andrea
Dieckmann, Alexander
Klein, Reinhard
Sahillioğlu, Yusuf
Metadata
Show full item record
Item Usage Stats
149
views
0
downloads
Cite This
A particularly challenging setting of the shape matching problem arises when the shapes being matched have topological artifacts due to the coalescence of spatially close surface regions – a scenario that frequently occurs when dealing with real data under suboptimal acquisition conditions. This track of the SHREC’16 contest evaluates shape matching algorithms that operate on 3D shapes under synthetically produced topological changes. The task is to produce a pointwise matching (either sparse or dense) between 90 pairs of shapes, representing the same individual in different poses but with different topology. A separate set of 15 shapes with ground-truth correspondence was provided as training data for learning-based techniques and for parameter tuning. Three research groups participated in the contest; this paper presents the track dataset, and describes the different methods and the contest results.
URI
https://hdl.handle.net/11511/81037
https://vision.in.tum.de/~laehner/shrec2016/dataset.php
https://user.ceng.metu.edu.tr/~ys/pubs/toponoise-3dor16.pdf
Conference Name
EG 3DOR Workshop, including SHREC'2016, 7 - 08 Mayıs 2016
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Matching of deformable shapes with topological noise
Lahner, Zorah; Rodola, Emanuele; Bronstein, Michael; Cremers, Daniel; Burghard, Oliver; Cosmo, Luca; Dieckmann, Alexander; Klein, Reinhard; Sahillioğlu, Yusuf (2016-05-08)
A particularly challenging setting of the shape matching problem arises when the shapes being matched have topological artifacts due to the coalescence of spatially close surface regions - a scenario that frequently occurs when dealing with real data under suboptimal acquisition conditions. This track of the SHREC'16 contest evaluates shape matching algorithms that operate on 3D shapes under synthetically produced topological changes. The task is to produce a pointwise matching (either sparse or dense) betw...
Shape recognition with generalized beam angle statistics
Tola, OO; Arica, N; Yarman Vural, Fatoş Tunay (2004-04-30)
In this study, we develop a new shape descriptor and matching algorithm in order to find a given template shape in an edge detected image without performing boundary extraction. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by ma...
Multi-image region growing for integrating disparity maps
Leloglu, UĞUR MURAT; Halıcı, Uğur (1999-01-01)
In this paper, a multi-image region growing algorithm to obtain planar 3-D surfaces in the object space from multiple dense disparity maps, is presented. A surface patch is represented by a plane equation and a set of pixels in multiple images. The union of back projections of all pixels in the set onto the infinite plane, forms the surface patch. Thanks to that hybrid representation of planar surfaces, region growing (both region aggregation and region merging) is performed on all images simultaneously. Pl...
Shape recognition with generalized beam angle statistics
Tola, OO; Arica, N; Yarman-Vural, F (2004-01-01)
In this study, we develop a new shape descriptor and a matching algorithm in order to find a given template shape in an edge detected image without extracting the boundary. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by matchin...
Scale invariant representation of 2 5D data
AKAGUNDUZ, Erdem; ULUSOY PARNAS, İLKAY; BOZKURT, Nesli; Halıcı, Uğur (2007-06-13)
In this paper, a scale and orientation invariant feature representation for 2.5D objects is introduced, which may be used to classify, detect and recognize objects even under the cases of cluttering and/or occlusion. With this representation a 2.5D object is defined by an attributed graph structure, in which the nodes are the pit and peak regions on the surface. The attributes of the graph are the scales, positions and the normals of these pits and peaks. In order to detect these regions a "peakness" (or pi...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Z. Lahner et al., “SHREC 16 Matching of Deformable Shapes with Topological Noise,” presented at the EG 3DOR Workshop, including SHREC′2016, 7 - 08 Mayıs 2016, 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/81037.
