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Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds / Xiang Gao in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 72 - 84 Titre : Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds Type de document : Article/Communication Auteurs : Xiang Gao, Auteur ; Shuhan Shen, Auteur ; Yang Zhou, Auteur ; Hainan Cui, Auteur ; Lingjie Zhu, Auteur ; Zhanyi Hu, Auteur Année de publication : 2018 Article en page(s) : pp 72 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] architecture [Termes IGN] données localisées 3D [Termes IGN] fusion de données [Termes IGN] image aérienne [Termes IGN] image terrestre [Termes IGN] modèle 3D du site [Termes IGN] patrimoine immobilier [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] temple Résumé : (Auteur) Ancient Chinese architecture 3D digitalization and documentation is a challenging task for the image based modeling community due to its architectural complexity and structural delicacy. Currently, an effective approach to ancient Chinese architecture 3D reconstruction is to merge the two point clouds, separately obtained from ground and aerial images by the SfM technique. There are two understanding issues should be specially addressed: (1) it is difficult to find the point matches between the images from different sources due to their remarkable variations in viewpoint and scale; (2) due to the inevitable drift phenomenon in any SfM reconstruction process, the resulting two point clouds are no longer strictly related by a single similarity transformation as it should be theoretically. To address these two issues, a new point cloud merging method is proposed in this work. Our method has the following characteristics: (1) the images are matched by leveraging sparse mesh based image synthesis; (2) the putative point matches are filtered by geometrical consistency check and geometrical model verification; and (3) the two point clouds are merged via bundle adjustment by linking the ground-to-aerial tracks. Extensive experiments show that our method outperforms many of the state-of-the-art approaches in terms of ground-to-aerial image matching and point cloud merging. Numéro de notice : A2018-355 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.023 Date de publication en ligne : 08/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90589 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018091	RAB	Livre	Centre de documentation	En réserve L003	Disponible
081-2018093	DEP-EXM	Livre	LASTIG	Dépôt en unité	Exclu du prêt
081-2018092	DEP-EAF	Livre	Nancy	Dépôt en unité	Exclu du prêt

Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 85 - 96 Titre : Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning Type de document : Article/Communication Auteurs : Rui Zhang, Auteur ; Guangyun Li, Auteur ; Minglei Li, Auteur ; Li Wang, Auteur Année de publication : 2018 Article en page(s) : pp 85 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage profond [Termes IGN] détection du bâti [Termes IGN] fusion de données [Termes IGN] réseau neuronal convolutif [Termes IGN] scène 3D [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (Auteur) We address the issue of the semantic segmentation of large-scale 3D scenes by fusing 2D images and 3D point clouds. First, a Deeplab-Vgg16 based Large-Scale and High-Resolution model (DVLSHR) based on deep Visual Geometry Group (VGG16) is successfully created and fine-tuned by training seven deep convolutional neural networks with four benchmark datasets. On the val set in CityScapes, DVLSHR achieves a 74.98% mean Pixel Accuracy (mPA) and a 64.17% mean Intersection over Union (mIoU), and can be adapted to segment the captured images (image resolution 2832 ∗ 4256 pixels). Second, the preliminary segmentation results with 2D images are mapped to 3D point clouds according to the coordinate relationships between the images and the point clouds. Third, based on the mapping results, fine features of buildings are further extracted directly from the 3D point clouds. Our experiments show that the proposed fusion method can segment local and global features efficiently and effectively. Numéro de notice : A2018-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.022 Date de publication en ligne : 11/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90590 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018091	RAB	Livre	Centre de documentation	En réserve L003	Disponible
081-2018093	DEP-EXM	Livre	LASTIG	Dépôt en unité	Exclu du prêt
081-2018092	DEP-EAF	Livre	Nancy	Dépôt en unité	Exclu du prêt

In-situ measurements from mobile platforms: An emerging approach to address the old challenges associated with forest inventories / Xinlian Liang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 97 - 107 Titre : In-situ measurements from mobile platforms: An emerging approach to address the old challenges associated with forest inventories Type de document : Article/Communication Auteurs : Xinlian Liang, Auteur ; Antero Kukko, Auteur ; Juha Hyyppä, Auteur ; Matti Lehtomäki, Auteur ; Jiri Pyorala, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 97 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation de précision [Termes IGN] exhaustivité des données [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Termes IGN] lidar mobile [Termes IGN] semis de points [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Accurate assessments of forest resources rely on ground truth data that are collected via in-situ measurements, which are fundamental for all other statistical- and/or remote-sensing-based deductions on quantified forest attributes. The major bottleneck of the current in-situ observation system is that the data collection is time consuming, and, thus, limited in extent, which potentially biases any further inferences made. Consequently, conventional field-data-collection approaches can hardly keep pace with the coverage, scale and frequency required for contemporary and future forest inventories. In-situ measurements from mobile platforms seem to be a promising technique to solve this problem and are estimated at least 10 times faster than static techniques (e.g., terrestrial laser scanning, TLS) at the plot level. However, the mobile platforms are still at the very early stages of development, and it is unclear which three-dimensional (3D) forest measurements the mobile systems can provide and at what accuracy. This study presents a quantitative evaluation of the performance of mobile platforms in a variety of forest conditions and through a comparison with state-of-the-art static in-situ observations. Two mobile platforms were used to collect field data, where the same laser-scanning system was both mounted on top of a vehicle and wore by an operator. The static in-situ observation from TLS is used as a baseline for the evaluation. All point clouds involved were processed through the same processing chain and compared to conventional manual measurement. The evaluation results indicate that the mobile platforms can assess homogeneous forests as well as static observations, but they cannot yet assess heterogeneous forest as required by practical applications. The major challenge is twofold: mobile-data coverage and accuracy. Future research should focus on the robust registration techniques between strips, especially in complex forest conditions, since errors of data registration results in significant impacts on tree attributes estimation accuracy. In cases that the spatial inconstancy cannot be eliminated, attributes estimation in single strips, i.e., the multi-single-scan approach, is an alternative. Meanwhile, operator training deserves attention since the data quality from mobile platforms is partly determined by the operators’ selection of trajectory in the field. Numéro de notice : A2018-357 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.019 Date de publication en ligne : 18/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90591 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018091	RAB	Livre	Centre de documentation	En réserve L003	Disponible
081-2018093	DEP-EXM	Livre	LASTIG	Dépôt en unité	Exclu du prêt
081-2018092	DEP-EAF	Livre	Nancy	Dépôt en unité	Exclu du prêt

Three-dimensional building façade segmentation and opening area detection from point clouds / S.M. Iman Zolanvari in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 134 - 149 Titre : Three-dimensional building façade segmentation and opening area detection from point clouds Type de document : Article/Communication Auteurs : S.M. Iman Zolanvari, Auteur ; Debra F. Laefer, Auteur ; Atteyeh S. Natanzi, Auteur Année de publication : 2018 Article en page(s) : pp 134 - 149 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] reconstruction 3D du bâti [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] toit Résumé : (Auteur) Laser scanning generates a point cloud from which geometries can be extracted, but most methods struggle to do this automatically, especially for the entirety of an architecturally complex building (as opposed to that of a single façade). To address this issue, this paper introduces the Improved Slicing Method (ISM), an innovative and computationally-efficient method for three-dimensional building segmentation. The method is also able to detect opening boundaries even on roofs (e.g. chimneys), as well as a building’s overall outer boundaries using a local density analysis technique. The proposed procedure is validated by its application to two architecturally complex, historic brick buildings. Accuracies of at least 86% were achieved, with computational times as little as 0.53 s for detecting features from a data set of 5.0 million points. The accuracy more than rivalled the current state of the art, while being up to six times faster and with the further advantage of requiring no manual intervention or reliance on a priori information. Numéro de notice : A2018-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.004 Date de publication en ligne : 09/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90592 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018091	RAB	Livre	Centre de documentation	En réserve L003	Disponible
081-2018093	DEP-EXM	Livre	LASTIG	Dépôt en unité	Exclu du prêt
081-2018092	DEP-EAF	Livre	Nancy	Dépôt en unité	Exclu du prêt