Non-parametric Single View Reconstruction of Curved Objects Using Convex Optimization

Authors:
Martin R. Oswald;Eno Töppe;Kalin Kolev;Daniel Cremers
Affiliations:
Computer Science Department, University of Bonn, Germany;Computer Science Department, University of Bonn, Germany;Computer Science Department, University of Bonn, Germany;Computer Science Department, University of Bonn, Germany
Venue:
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Year:
2009

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Abstract

We propose a convex optimization framework delivering intuitive and reasonable 3D meshes from a single photograph. For a given input image, the user can quickly obtain a segmentation of the object in question. Our algorithm then automatically generates an admissible closed surface of arbitrary topology without the requirement of tedious user input. Moreover we provide a tool by which the user is able to interactively modify the result afterwards through parameters and simple operations in a 2D image space. The algorithm targets a limited but relevant class of real world objects. The object silhouette and the additional user input enter a functional which can be optimized globally in a few seconds using recently developed convex relaxation techniques parallelized on state-of-the-art graphics hardware.