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Approximate Registration of Point Clouds with Large Scale Differences : Volume Ii-5/W2, Issue 1 (16/10/2013)

By Novak, D.

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Book Id: WPLBN0004014120
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Approximate Registration of Point Clouds with Large Scale Differences : Volume Ii-5/W2, Issue 1 (16/10/2013)  
Author: Novak, D.
Volume: Vol. II-5/W2, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Schindler, K., & Novak, D. (2013). Approximate Registration of Point Clouds with Large Scale Differences : Volume Ii-5/W2, Issue 1 (16/10/2013). Retrieved from http://www.ebooklibrary.org/


Description
Description: Institute of Geodesy and Photogrammetry, ETH Zürich, 8093 Zürich, Switzerland. 3D reconstruction of objects is a basic task in many fields, including surveying, engineering, entertainment and cultural heritage. The task is nowadays often accomplished with a laser scanner, which produces dense point clouds, but lacks accurate colour information, and lacks per-point accuracy measures. An obvious solution is to combine laser scanning with photogrammetric recording. In that context, the problem arises to register the two datasets, which feature large scale, translation and rotation differences. The absence of approximate registration parameters (3D translation, 3D rotation and scale) precludes the use of fine-registration methods such as ICP. Here, we present a method to register realistic photogrammetric and laser point clouds in a fully automated fashion. The proposed method decomposes the registration into a sequence of simpler steps: first, two rotation angles are determined by finding dominant surface normal directions, then the remaining parameters are found with RANSAC followed by ICP and scale refinement. These two steps are carried out at low resolution, before computing a precise final registration at higher resolution.

Summary
Approximate registration of point clouds with large scale differences

 

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