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Evaluating the 3D integrity of underwater structure from motion workflows / Ian M. Lochhead in Photogrammetric record, vol 37 n° 177 (March 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 177 (March 2022) . - pp 35 - 60 Titre : Evaluating the 3D integrity of underwater structure from motion workflows Type de document : Article/Communication Auteurs : Ian M. Lochhead, Auteur Année de publication : 2022 Article en page(s) : pp 35 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] auscultation d'ouvrage [Termes IGN] chaîne de traitement [Termes IGN] étalonnage d'instrument [Termes IGN] fond marin [Termes IGN] image sous-marine [Termes IGN] modélisation 3D [Termes IGN] Pacifique nord [Termes IGN] récif corallien [Termes IGN] semis de points [Termes IGN] semis de points clairsemés [Termes IGN] structure-from-motion Résumé : (auteur) Structure from motion (SfM) is an accessible and non-intrusive method of three-dimensional (3D) data capture popular for tropical coral reef surveying. In the north-east Pacific Ocean, where there are many environmentally sensitive benthic organisms whose morphology and function are equally important, SfM surveys are less commonly studied. Temperate waters pose unique challenges to SfM workflows, which must be systematically unpacked to understand their impact on data quality and veracity. This uncertainty raises broader questions concerning SfM as a spatial data-acquisition and ecological characterisation method in temperate waters, and whether a systematic workflow assessment reveals vital relationships between SfM implementation parameters, 3D data products and their implications for underwater SfM surveys. This paper, the second of two empirical assessments, reports on a series of wet-lab and dryland tests quantifying the impact that temperate waters, underwater cameras, and photograph quantity and configuration have on SfM accuracy. These tests provided crucial accuracy benchmarks informing subsequent field-based surveys and revealed that underwater SfM workflows can generate highly accurate 3D models in temperate waters. Numéro de notice : A2022-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : doi.org/10.1111/phor.12399 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1111/phor.12399 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100216 [article]

Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 177 (March 2022) . - pp 87 - 110 Titre : Traffic sign three-dimensional reconstruction based on point clouds and panoramic images Type de document : Article/Communication Auteurs : Minye Wang, Auteur ; Rufei Liu, Auteur ; Jiben Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 87 - 110 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] correction d'image [Termes IGN] extraction de traits caractéristiques [Termes IGN] image panoramique [Termes IGN] lidar mobile [Termes IGN] reconstruction 3D [Termes IGN] semis de points [Termes IGN] signalisation routière Résumé : (auteur) Traffic signs are a very important source of information for drivers and pilotless automobiles. With the advance of Mobile LiDAR System (MLS), massive point clouds have been applied in three-dimensional digital city modelling. However, traffic signs in MLS point clouds are low density, colourless and incomplete. This paper presents a new method for the reconstruction of vertical rectangle traffic sign point clouds based on panoramic images. In this method, traffic sign point clouds are extracted based on arc feature and spatial semantic features analysis. Traffic signs in images are detected by colour and shape features and a convolutional neural network. Traffic sign point cloud and images are registered based on outline features. Finally, traffic sign points match traffic sign pixels to reconstruct the traffic sign point cloud. Experimental results have demonstrated that this proposed method can effectively obtain colourful and complete traffic sign point clouds with high resolution. Numéro de notice : A2022-254 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12398 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.1111/phor.12398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100217 [article]

Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces / Ali Karami in Photogrammetric record, vol 37 n° 177 (March 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 177 (March 2022) . - pp 111 - 138 Titre : Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces Type de document : Article/Communication Auteurs : Ali Karami, Auteur ; Fabio Menna, Auteur ; Fabio Remondino, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 111 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] appariement d'images [Termes IGN] axe de prise de vue [Termes IGN] étalonnage [Termes IGN] figure géométrique [Termes IGN] point d'appui [Termes IGN] points homologues [Termes IGN] rayonnement lumineux [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points Résumé : (auteur) Three-dimensional (3D) measurement of non-collaborative surfaces is still an open research topic. This paper investigates and quantifies for the first time the effect of light directionality and fusion of multiple images as a method to improve the quality of photogrammetric 3D reconstruction. For this aim, an image acquisition system that employs multiple light sources was developed to highlight the roughness and microstructures of the object under investigation. Images were captured at various grazing angles to highlight the local surface roughness and microstructures. Individual point clouds, created using images taken at different grazing angles, were produced using dense image-matching techniques. These point clouds were then compared against different 3D photogrammetric reconstructions obtained from a pre-processing of the acquired images based on diffuse lighting, median and average images. Experiments showed that exploiting light directionality significantly improves image-matching quality. Furthermore, depending on the light direction, the root mean square (RMS) error of the 3D surfaces obtained using the proposed system were up to 50% less than those created by traditional diffuse lighting. Numéro de notice : A2022-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12400 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1111/phor.12400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100218 [article]