IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Fast point feature histograms (FPFH) for 3D registration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
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Local description of images is a common technique in many computer vision related research. Due to recent improvements in RGB-D cameras, local description of 3D data also becomes practical. The number of studies that make use of this extra information is increasing. However, their applicabilities are limited due to the need for generic combination methods. In this paper, we propose combining textural and geometrical descriptors for scene recognition of RGB-D data. The methods together with the normalization stages proposed in this paper can be applied to combine any descriptors obtained from 2D and 3D domains. This study represents and evaluates different ways of combining multi-modal descriptors within the BoW approach in the context of indoor scene localization. Query's rough location is determined from the pre-recorded images and depth maps in an unsupervised image matching manner.