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An improved segmentation approach for planar surfaces from undestructured 3D point clouds / T.M. Awwad in Photogrammetric record, vol 25 n° 129 (March - May 2010)  

Public [article]  inPhotogrammetric record > vol 25 n° 129 (March - May 2010) . - pp 5 - 23 Titre : An improved segmentation approach for planar surfaces from undestructured 3D point clouds Type de document : Article/Communication Auteurs : T.M. Awwad, Auteur ; Q. Zhu, Auteur ; Z. Du, Auteur ; Y. Zhang, Auteur Année de publication : 2010 Article en page(s) : pp 5 - 23 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] Ransac (algorithme) [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] surface plane Résumé : (Auteur) The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The features generally include the position of each point (X, Y and Z), locally estimated surface normals and residuals of best fitting surfaces; however, these features could be affected by noisy points and in consequence directly affect the segmentation results. Therefore, massive unstructured and noisy point clouds also lead to bad segmentation (over-segmentation, under-segmentation or no segmentation). While the RANSAC (random sample consensus) algorithm is effective in the presence of noise and outliers, it has two significant disadvantages, namely, its efficiency and the fact that the plane detected by RANSAC may not necessarily belong to the same object surface; that is, spurious surfaces may appear, especially in the case of parallel-gradual planar surfaces such as stairs. The innovative idea proposed in this paper is a modification for the RANSAC algorithm called Seq-NV-RANSAC. This algorithm checks the normal vector (NV) between the existing point clouds and the hypothesised RANSAC plane, which is created by three random points, under an intuitive threshold value. After extracting the first plane, this process is repeated sequentially (Seq) and automatically, until no planar surfaces can be extracted from the remaining points under the existing threshold value. This prevents the extraction of spurious surfaces, brings an improvement in quality to the computed attributes and increases the degree of automation of surface extraction. Thus the best fit is achieved for the real existing surfaces. Copyright RS&PS + Blackwell Publishing Numéro de notice : A2010-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2009.00564.x Date de publication en ligne : 11/03/2010 En ligne : https://doi.org/10.1111/j.1477-9730.2009.00564.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30256 [article]

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