3D Object Modeling and Segmentation Based on Edge-Point Matching with Local Descriptors

Authors:
Masahiro Tomono
Affiliations:
Chiba Institute of Technology, Narashino, Japan 275-0016
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

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Abstract

3D object modeling is a crucial issue for environment recognition. A difficult problem is how to separate objects from the background clutter. This paper presents a method of 3D object modeling and segmentation from images for specific object recognition. An object model is composed of edge points which are reconstructed using a structure-from-motion technique. A SIFT descriptor is attached to each edge point for object recognition. The object of interest is segmented by finding the edge points which co-occur in images with different backgrounds. Experimental results show that the proposed method creates detailed 3D object models successfully.