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Automatic 3D Mapping Using Multiple Uncalibrated Close Range Images : Volume Xl-1/W3, Issue 1 (24/09/2013)

By Rafiei, M.

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Book Id: WPLBN0004014492
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Automatic 3D Mapping Using Multiple Uncalibrated Close Range Images : Volume Xl-1/W3, Issue 1 (24/09/2013)  
Author: Rafiei, M.
Volume: Vol. XL-1/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2013
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Rafiei, M., & Saadatseresht, M. (2013). Automatic 3D Mapping Using Multiple Uncalibrated Close Range Images : Volume Xl-1/W3, Issue 1 (24/09/2013). Retrieved from http://www.ebooklibrary.org/


Description
Description: Department of Surveying Engineering, University of Tehran, Iran. Automatic three-dimensions modeling of the real world is an important research topic in the geomatics and computer vision fields for many years. By development of commercial digital cameras and modern image processing techniques, close range photogrammetry is vastly utilized in many fields such as structure measurements, topographic surveying, architectural and archeological surveying, etc. A non-contact photogrammetry provides methods to determine 3D locations of objects from two-dimensional (2D) images. Problem of estimating the locations of 3D points from multiple images, often involves simultaneously estimating both 3D geometry (structure) and camera pose (motion), it is commonly known as structure from motion (SfM). In this research a step by step approach to generate the 3D point cloud of a scene is considered. After taking images with a camera, we should detect corresponding points in each two views. Here an efficient SIFT method is used for image matching for large baselines. After that, we must retrieve the camera motion and 3D position of the matched feature points up to a projective transformation (projective reconstruction). Lacking additional information on the camera or the scene makes the parallel lines to be unparalleled. The results of SfM computation are much more useful if a metric reconstruction is obtained. Therefor multiple views Euclidean reconstruction applied and discussed. To refine and achieve the precise 3D points we use more general and useful approach, namely bundle adjustment. At the end two real cases have been considered to reconstruct (an excavation and a tower).

Summary
AUTOMATIC 3D MAPPING USING MULTIPLE UNCALIBRATED CLOSE RANGE IMAGES

 

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