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Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 17 - 28 Titre : Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Li Li, Auteur ; Nan Song, Auteur ; Fei Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation 3D [Termes IGN] Perceptron multicouche [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] toit Résumé : (auteur) Three-dimensional (3D) building roof reconstruction from airborne LiDAR point clouds is an important task in photogrammetry and computer vision. To automatically reconstruct the 3D building models at Level of Detail 2 (LoD-2) from airborne LiDAR point clouds, the data-driven approaches usually need to be performed in two steps: geometric primitive extraction and roof structure inference. Obviously, the traditional approaches are not end-to-end, the accumulated errors in different stages cannot be avoided and the final 3D roof models may not be optimal. In addition, the results of 3D roof models largely depend on the accuracy of geometric primitives (planes, lines, etc.). To solve these problems, we present a deep learning-based approach to directly reconstruct building roofs from airborne LiDAR point clouds, named Point2Roof. In our method, we start by extracting the deep features for each input point using PointNet++. Then, we identify a set of candidate corner points from the input point clouds using the extracted deep features. In addition, we also regress the offset for each candidate corner point to refine their locations. After that, these candidates are clustered into a set of initial vertices, and we further refine their locations to obtain the final accurate vertices. Finally, we propose a Paired Point Attention (PPA) module to predict the true model edges from an exhaustive set of candidate edges between the vertices. Unlike traditional roof modeling approaches, the proposed Point2Roof is end-to-end. However, due to the lack of a building reconstruction dataset, we construct a large-scale synthetic dataset to verify the effectiveness and robustness of the proposed Point2Roof. The experimental results conducted on the synthetic benchmark demonstrate that the proposed Point2Roof significantly outperforms the traditional roof modeling approaches. The experiments also show that the network trained on the synthetic dataset can be applied to the real point clouds after fine-tuning the trained model on a small real dataset. The large-scale synthetic dataset, the small real dataset and the source code of our approach are publicly available in https://github.com/Li-Li-Whu/Point2Roof. Numéro de notice : A2022-745 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.027 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101728 [article]

Extraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 749 - 768 Titre : Extraction of street pole-like objects based on plane filtering from mobile LiDAR data Type de document : Article/Communication Auteurs : Jingming Tu, Auteur ; Jian Yao, Auteur ; Li Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 749 - 768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse d'image orientée objet [Termes IGN] carte routière [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] forme caractéristique [Termes IGN] méthode robuste [Termes IGN] octree [Termes IGN] réseau routier Résumé : (auteur) Pole-like objects provide important street infrastructure for road inventory and road mapping. In this article, we proposed a novel pole-like object extraction algorithm based on plane filtering from mobile Light Detection and Ranging (LiDAR) data. The proposed approach is composed of two parts. In the first part, a novel octree-based split scheme was proposed to fit initial planes from off-ground points. The results of the plane fitting contribute to the extraction of pole-like objects. In the second part, we proposed a novel method of pole-like object extraction by plane filtering based on local geometric feature restriction and isolation detection. The proposed approach is a new solution for detecting pole-like objects from mobile LiDAR data. The innovation in this article is that we assumed that each of the pole-like objects can be represented by a plane. Thus, the essence of extracting pole-like objects will be converted to plane selecting problem. The proposed method has been tested on three data sets captured from different scenes. The average completeness, correctness, and quality of our approach can reach up to 87.66%, 88.81%, and 79.03%, which is superior to state-of-the-art approaches. The experimental results indicate that our approach can extract pole-like objects robustly and efficiently. Numéro de notice : A2021-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2993454 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2993454 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96758 [article]

Seamline network generation based on foreground segmentation for orthoimage mosaicking / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 41 - 53 Titre : Seamline network generation based on foreground segmentation for orthoimage mosaicking Type de document : Article/Communication Auteurs : Li Li, Auteur ; Jingming Tu, Auteur ; Ye Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 41 - 53 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme Graph-Cut [Termes IGN] mosaïquage d'images [Termes IGN] optimisation (mathématiques) [Termes IGN] orthoimage [Termes IGN] orthophotoplan numérique [Termes IGN] raccord d'images Résumé : (Auteur) For multiple orthoimages mosaicking, the detection of an optimal seamline in an overlapped region and the generation of a seamline network are two key issues for creating a seamless and pleasant large-scale digital orthophoto map. In this paper, a novel system is proposed to generate the large-scale orthophoto by mosaicking multiple orthoimages via Graph cuts. The proposed system is comprised of two parts. In the first part, to ensure that the detected seamline avoids crossing the obvious objects, a novel foreground segmentation-based approach is proposed to detect the optimal seamline for two adjacent images. The foreground objects are segmented from the overlapped region at the superpixel level followed by the pixel-level seamline optimization. In the second part, we propose a novel seamline network generation approach to produce the large-scale orthophoto by mosaicking multiple orthoimages. The pairwise and junction regions extracted from the initial network are refined using two-label and multi-label Graph cuts, respectively. The key advantage of our proposed seamline network is that junction points can be automatically and optimally found using the multi-label Graph cuts. The experimental results on two groups of orthoimages show that our proposed system can generate high-quality seamline networks with less artifacts, and that it outperforms the state-of-the-art algorithm and the commercial software based on visual comparison and statistical evaluation. Numéro de notice : A2019-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.12.002 Date de publication en ligne : 20/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.12.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92158 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt