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Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data

Anne-Laure Chauve 1, 2 Patrick Labatut 2, 1 Jean-Philippe Pons 1, 2
1 imagine [Marne-la-Vallée]
ligm - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : In this paper, we present a novel method, the first to date to our knowledge, which is capable of directly and automatically producing a concise and idealized 3D representation from unstructured point data of complex cluttered real-world scenes, with a high level of noise and a significant proportion of outliers, such as those obtained from passive stereo. Our algorithm can digest millions of input points into an optimized lightweight watertight polygonal mesh free of self-intersection, that preserves the structural components of the scene at a user-defined scale, and completes missing scene parts in a plausible manner. To achieve this, our algorithm incorporates priors on urban and architectural scenes, notably the prevalence of vertical structures and orthogonal intersections. A major contribution of our work is an adaptive decomposition of 3D space induced by planar primitives, namely a polyhedral cell complex. We experimentally validate our approach on several challenging noisy point clouds of urban and architectural scenes.
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Submitted on : Wednesday, December 21, 2011 - 6:49:20 PM
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Anne-Laure Chauve, Patrick Labatut, Jean-Philippe Pons. Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data. CVPR 2010, Twenty-Third IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun 2010, San Francisco, United States. pp.1261-1268, ⟨10.1109/CVPR.2010.5539824⟩. ⟨hal-00654408⟩



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