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Localization of Windows and Doors in 3D Point Clouds of Facades : Volume Ii-3, Issue 1 (07/08/2014)

By Nguatem, W.

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Book Id: WPLBN0003981091
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Localization of Windows and Doors in 3D Point Clouds of Facades : Volume Ii-3, Issue 1 (07/08/2014)  
Author: Nguatem, W.
Volume: Vol. II-3, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Nguatem, W., Mayer, H., & Drauschke, M. (2014). Localization of Windows and Doors in 3D Point Clouds of Facades : Volume Ii-3, Issue 1 (07/08/2014). Retrieved from

Description: Institute of Applied Computer Science Bundeswehr University Munich Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany. In this paper, we present a fully automatic approach to localize the outlines of facade objects (windows and doors) in 3D point clouds of facades. We introduce an approach to search for the main facade wall and locate the facade objects within a probabilistic framework. Our search routine is based on Monte Carlo Simulation (MC-Simulation). Templates containing control points of curves are used to approximate the possible shapes of windows and doors. These are interpolated using parametric B-spline curves. These templates are scored in a sliding window style over the entire facade using a likelihood function in a probabilistic matching procedure. This produces many competing results for which a two layered model selection based on Bayes factor is applied. A major thrust in our work is the introduction of a 2D shape-space of similar shapes under affine transform in this architectural scene. This transforms the initial parametric B-splines curves representing the outlines of objects to curves of affine similarity in a strongly reduced dimensionality thus facilitating the generation of competing hypotheses within the search space. A further computational speedup is achieved through the clustering of the search space to disjoint regions, thus enabling a parallel implementation. We obtain state-of-the results on self-acquired data sets. The robustness of our algorithm is evaluated on 3D point clouds from image matching and LiDAR data of diverse quality.

Localization of Windows and Doors in 3d Point Clouds of Facades


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