3D object modeling and segmentation based on edge-point matching with local descriptors

Abstract

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. © Springer-Verlag Berlin Heidelberg 2008.