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High Accuracy and Visibility-Consistent Dense Multiview Stereo

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4 Author(s)
Vu, H.-H. ; Ecole des Fonts ParisTech, Univ. Paris-Est, Marne-la-Valle''e, France ; Labatut, P. ; Pons, J.-P. ; Keriven, R.

Since the initial comparison of Seitz et al. [48], 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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 5 )