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Automatic roof model reconstruction from ALS data and 2D ground plans based on side projection and the TMR [TIN-Merging and Reshaping] algorithm / J. Rau in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 66 n° 6 supplement (December 2011) . - pp 13 - 27 Titre : Automatic roof model reconstruction from ALS data and 2D ground plans based on side projection and the TMR [TIN-Merging and Reshaping] algorithm Type de document : Article/Communication Auteurs : J. Rau, Auteur ; B. Lin, Auteur Année de publication : 2011 Article en page(s) : pp 13 - 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] fusion de données [Termes IGN] lasergrammétrie [Termes IGN] modélisation 3D [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté [Termes IGN] toit [Termes IGN] Triangulated Irregular Network Résumé : (Auteur) This paper presents an automatic roof model reconstruction method based on the side projection of airborne laser scanning (ALS) data. The proposed approach first detects the building’s primary orientation and decomposes multi-layer roofs into a single one. Then, 3D structural lines are detected and restored from the projected point clouds. Finally, a line-based roof model reconstruction algorithm, namely TIN-Merging and Reshaping (TMR), is proposed. The originality for 3D roof modeling is to perform geometric analysis and topology reconstruction from two 2D projections and then reshapes the roof using elevation information from the 3D structural lines or ALS data. Experimental results indicate a nearly 100% success rate for topology reconstruction can be achieved provided that the 3D structural lines can be enclosed as polygons. However, the success rate of the Reshaping stage is dependent on the complexity of the rooftop structure. With the exception of domed and multiple orientations roofs, which are not considered in the developed method, we achieve success rates around 92–95%. As for absolute accuracy, less that 50 cm of root-mean-square error is observed in all X–Y–Z directions. The results demonstrate that the proposed scheme is robust and accurate even when a group of connected buildings with multiple layers and mixed roof types is processed. Numéro de notice : A2011-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2011.09.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2011.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31409 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2011071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Building feature extraction from airborne lidar data based on tensor voting algorithm / R. You in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 12 (December 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 12 (December 2011) . - pp 1221 - 1231 Titre : Building feature extraction from airborne lidar data based on tensor voting algorithm Type de document : Article/Communication Auteurs : R. You, Auteur ; B. Lin, Auteur Année de publication : 2011 Article en page(s) : pp 1221 - 1231 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] calcul tensoriel [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] valeur propre Résumé : (Auteur) This study presents a novel approach based on the tensor voting framework for extracting building features from airborne lidar data. Geometric features of lidar points are represented by a tensor field in this paper. For the extraction of roof patches, a region-growing method with principal features is developed from the properties of eigenvalues and eigenvectors of the tensor field. Additionally, three new indicators for the strength of features are presented to reduce the effect of the number of points on feature identification, and a supervised method is proposed to determine the threshold of planar feature strength for the region-growing. The extraction of ridge and edge lines from the segmented roof patches is also discussed. Experiments based on airborne lidar data are described to demonstrate the effectiveness of the proposed method, with those the results compared with the PCA method. Numéro de notice : A2011-487 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.12.1221 En ligne : https://doi.org/10.14358/PERS.77.12.1221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31381 [article]

A quality prediction method for building model reconstruction using LiDAR data and topographic maps / R. You in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3471 - 3480 Titre : A quality prediction method for building model reconstruction using LiDAR data and topographic maps Type de document : Article/Communication Auteurs : R. You, Auteur ; B. Lin, Auteur Année de publication : 2011 Article en page(s) : pp 3471 - 3480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] calcul tensoriel [Termes IGN] carte topographique [Termes IGN] conflation [Termes IGN] données lidar [Termes IGN] fusion de données multisource [Termes IGN] indicateur de qualité [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit Résumé : (Auteur) This paper integrates light detection and ranging (LiDAR) data and topographic maps and predicts the quality of 3D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction. Numéro de notice : A2011-364 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2128326 Date de publication en ligne : 12/05/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2128326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31143 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011091	RAB	Revue	Centre de documentation	En réserve L003	Disponible