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Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains

Abstract : In this article, we present a method for detection and classification of artifacts at the street level, in order to filter cloud point, facilitating the urban modeling process. Our approach exploits 3D information by using range image, a projection of 3D points onto an image plane where the pixel intensity is a function of the measured distance between 3D points and the plane. By assuming that the artifacts are on the ground, they are detected using a Top-Hat of the hole filling algorithm of range images. Then, several features are extracted from the detected connected components and a stepwise forward variable/model selection by using the Wilk's Lambda criterion is performed. Afterward, CCs are classified in four categories (lampposts, pedestrians, cars and others) by using a supervised machine learning method. The proposed method was tested on cloud points of Paris, and have shown satisfactory results on the whole dataset.
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  • HAL Id : hal-00833557, version 1
  • ARXIV : 1306.3084

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Jorge Hernandez, Beatriz Marcotegui. Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains. Revue française de photogrammetrie et de télédection, 2008, 191, pp.28-35. ⟨hal-00833557⟩

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