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Comparison of 3D local and global descriptors for similarity retrieval of range data
Date
2016-04-05
Author
Bayramoglu, Neslihan
Alatan, Abdullah Aydın
Metadata
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Recent improvements in scanning technologies such as consumer penetration of RGB-D cameras lead obtaining and managing range image databases practical. Hence, the need for describing and indexing such data arises. In this study, we focus on similarity indexing of range data among a database of range objects (range-to-range retrieval) by employing only single view depth information. We utilize feature based approaches both on local and global scales. However, the emphasis is on the local descriptors with their global representations. A comparative study with extensive experimental results is presented. In addition, we introduce a publicly available range object database which is large and has a high diversity that is suitable for similarity retrieval applications. The simulation results indicate competitive performance between local and global methods. While better complexity trade-off can be achieved with the global techniques, local methods perform better in distinguishing different parts of incomplete depth data.
Subject Keywords
Range data retrieval
,
Local descriptors
,
Global descriptors
,
Similarity indexing
,
Single view depth data description
URI
https://hdl.handle.net/11511/33401
Journal
NEUROCOMPUTING
DOI
https://doi.org/10.1016/j.neucom.2015.08.105
Collections
Department of Electrical and Electronics Engineering, Article
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N. Bayramoglu and A. A. Alatan, “Comparison of 3D local and global descriptors for similarity retrieval of range data,”
NEUROCOMPUTING
, pp. 13–27, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33401.