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High Accuracy and Visibility-Consistent Dense Multiview Stereo

Hoang-Hiep Vu 1, 2 Patrick Labatut 3 Jean-Philippe Pons 4 Renaud Keriven 4 
1 imagine [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : Since the initial comparison of Seitz et al., the accuracy of dense multiview stereovision methods has been increasing steadily. A number of limitations, however, make most of these methods not suitable to outdoor scenes taken under uncontrolled imaging conditions. The present work consists of a complete dense multiview stereo pipeline which circumvents these limitations, being able to handle large-scale scenes without sacrificing accuracy. Highly detailed reconstructions are produced within very reasonable time thanks to two key stages in our pipeline: a minimum s-t cut optimization over an adaptive domain that robustly and efficiently filters a quasidense point cloud from outliers and reconstructs an initial surface by integrating visibility constraints, followed by a mesh-based variational refinement that captures small details, smartly handling photo-consistency, regularization, and adaptive resolution. The pipeline has been tested over a wide range of scenes: from classic compact objects taken in a laboratory setting, to outdoor architectural scenes, landscapes, and cultural heritage sites. The accuracy of its reconstructions has also been measured on the dense multiview benchmark proposed by Strecha et al. [59], showing the results to compare more than favorably with the current state-of-the-art methods.
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Soumis le : mardi 26 juin 2012 - 15:51:13
Dernière modification le : samedi 15 janvier 2022 - 03:58:18



Hoang-Hiep Vu, Patrick Labatut, Jean-Philippe Pons, Renaud Keriven. High Accuracy and Visibility-Consistent Dense Multiview Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34 (5), pp.889-901. ⟨10.1109/TPAMI.2011.172⟩. ⟨hal-00712178⟩



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