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Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
Titre : Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization Type de document : Article/Communication Auteurs : Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 57 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire de Markov
[Termes IGN] données lidar
[Termes IGN] espace intérieur
[Termes IGN] maillage
[Termes IGN] programmation linéaire
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] vectorisationRésumé : (Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. Numéro de notice : A2021-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.019 date de publication en ligne : 15/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779
in ISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74[article]
Titre : Activity recognition in residential spaces with Internet of things devices and thermal imaging Type de document : Article/Communication Auteurs : Kshirasagar Naik, Auteur ; Tejas Pandit, Auteur ; Nitin Naik, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compréhension de l'image
[Termes IGN] contrôle par télédétection
[Termes IGN] détection d'événement
[Termes IGN] espace intérieur
[Termes IGN] image RVB
[Termes IGN] image thermique
[Termes IGN] intelligence artificielle
[Termes IGN] internet des objets
[Termes IGN] itération
[Termes IGN] modèle stéréoscopique
[Termes IGN] objet mobile
[Termes IGN] reconnaissance automatique
[Termes IGN] reconnaissance d'objets
[Termes IGN] scène 3DRésumé : (auteur) In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces. Numéro de notice : A2021-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21030988 date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/s21030988 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97075
in Sensors > vol 21 n° 3 (February 2021) . - n° 988[article]
Titre : Method for generation of indoor GIS models based on BIM models to support adjacent analysis of indoor spaces Type de document : Article/Communication Auteurs : Qingxiang Chen, Auteur ; Jing Chen, Auteur ; Wumeng Huang, Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] adjacence
[Termes IGN] CityGML
[Termes IGN] espace intérieur
[Termes IGN] indoorGML
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] requête spatialeRésumé : (auteur) Methods for the generation of indoor geographic information system (GIS) models based on building information modelling (BIM) models can promote the analysis and application of indoor GIS, avoiding the complexity of traditional indoor space collection. The indoor adjacency relations (i.e., the attribute of IndoorGML) play a vital role in the adjacent query and analysis in indoor GIS applications (i.e., obtaining the neighbors or affected spaces of a cellular space in a building). However, current methods ignore the important feature, which considerably limits the spatial analysis ability of indoor GIS. Therefore, we developed a method for the generation of indoor GIS models based on BIM models to support adjacent analysis of indoor spaces. The method first devised an indoor GIS model (IGSM) by integrating spatial features (mainly adjacency relations) and the BIM model. Then, we proposed rapid modeling algorithms to mainly establish indoor adjacency relations based on the IGSM. Moreover, in the potential application of indoor GIS (e.g., indoor emergency response), we proposed a K-adjacent analysis algorithm to improve the application ability of the adjacent analysis of indoor GIS. Finally, experimental results suggest its validity and efficiency, which has substantial practical significance for the subsequent analysis and application of 3D GIS. Numéro de notice : A2020-662 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090508 date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.3390/ijgi9090508 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96138
in ISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - 24 p.[article]
Titre : A review of techniques for 3D reconstruction of indoor environments Type de document : Article/Communication Auteurs : Zhizhong Kang, Auteur ; Juntao Yang, Auteur ; Zhou Yang, Auteur ; Sai Cheng, Auteur Année de publication : 2020 Article en page(s) : 31 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] Cartographie et localisation simultanées
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace intérieur
[Termes IGN] image RVB
[Termes IGN] indoorGML
[Termes IGN] jeu de données localisées
[Termes IGN] modèle géométrique
[Termes IGN] modèle sémantique de données
[Termes IGN] modèle topologique de données
[Termes IGN] reconstruction 3DRésumé : (auteur) Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand. Numéro de notice : A2020-299 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050330 date de publication en ligne : 19/05/2020 En ligne : https://doi.org/10.3390/ijgi9050330 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95139
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 31 p.[article]
Titre : An indoor spatial accessible area generation approach considering distance constraints Type de document : Article/Communication Auteurs : Lina Yang, Auteur ; Hongru Bi, Auteur ; Xiaojing Yao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 25 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] analyse spatiale
[Termes IGN] contrainte géométrique
[Termes IGN] distance
[Termes IGN] espace intérieur
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] modèle géométrique
[Termes IGN] zone tamponRésumé : (auteur) Indoor objects’ accessible area generation considering distance constraints is of important realistic significance for spatial analysis. To the best of our knowledge, few studies have been conducted on indoor accessible area generation considering distance constraints. And because of different spatial characteristics between indoor and outdoor environment, the commonly used approach in outdoor space is not suitable for indoor space. In this paper, based on the hybrid spatial data model of geometric and symbolic model, an accessible area generation approach considering distance constraints for indoor environment is proposed by improving traditional spatial buffer zone generation technique. The buffer zone generation with a predefined distance around indoor objects within their located subspace is executed first; then, based on the indoor spatial connectivity, buffer generation around exit points is successively executed in its next connected subspaces until the distance decreases to zero. The merge of these generated buffer zones is the result of accessible area generation with a predefined distance constraint. During the process, two kinds of spatial search strategies, depth-first search and breadth-first search, are presented. Two sets of experiments are conducted to validate the correctness and efficiency of the proposed approach. Results show that the approach can be effectively used to solve the problem of indoor objects’ accessible area generation with distance constraints. Moreover, the potential use, as well as the limitation of the proposed approach is discussed in this paper. Numéro de notice : A2020-118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2019.1680575 date de publication en ligne : 18/10/2019 En ligne : https://doi.org/10.1080/19475683.2019.1680575 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94737
in Annals of GIS > Vol 26 n° 1 (January 2020) . - pp 25 - 34[article]Contribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)
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