Article

Unique signatures of histograms for local surface description

Authors:

Federico Tombari,

Luigi Di StefanoAuthors Info & Claims

ECCV'10: Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III

Pages 356 - 369

Published: 05 September 2010 Publication History

Abstract

This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.

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Unique signatures of histograms for local surface description
1. Computing methodologies
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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECCV'10: Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III

September 2010

803 pages

ISBN:364215557X

Editors:
Kostas Daniilidis
GRASP Laboratory, University of Pennsylvania, Philadelphia, PA
,
Petros Maragos
National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece
,
Nikos Paragios
Ecole Centrale de Paris, Department of Applied Mathematics, Chatenay-Malabry, France

Sponsors

Google Inc.
Microsoft Research: Microsoft Research
technicolor
INRIA: Institut Natl de Recherche en Info et en Automatique
IBM: IBM

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 September 2010

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Zhao XZeng XGao LXu YWang YZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)DMGC:Deep Triangle Mesh Geometry Compression via Connectivity PredictionProceedings of the 33rd Workshop on Network and Operating System Support for Digital Audio and Video10.1145/3592473.3592561(15-21)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3592473.3592561
Kamhoua BZhang LChen YYang HMa KHan BLi BCheng JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Exact shape correspondence via 2D graph convolutionProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601584(18072-18087)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601584
Yang FGuo LChen ZTao WKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)One-inlier is firstProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600776(6982-6995)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600776
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