Issue No. 05 - May (2012 vol. 34)
Hoang-Hiep Vu , Ecole des Ponts, Paris
Patrick Labatut , Ecole des Ponts, Paris
Jean-Philippe Pons , Ecole des Ponts, Paris
Renaud Keriven , Ecole des Ponts, Paris
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. , showing the results to compare more than favorably with the current state-of-the-art methods.
Dense multiview stereo, surface reconstruction, large-scale scenes, minimum s-t cut, deformable mesh.
R. Keriven, P. Labatut, H. Vu and J. Pons, "High Accuracy and Visibility-Consistent Dense Multiview Stereo," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 889-901, 2011.