A New Variational Framework for Multiview Surface Reconstruction

The creation of surfaces from overlapping images taken from different vantages is a hard and important problem in computer vision. Recent developments fall primarily into two categories: the use of dense matching to produce point clouds from which surfaces are built, and the construction of surfaces from images directly. This paper presents a new method for surface reconstruction falling in the second category. First, a strongly motivated variational framework is built from the ground up based on a limiting case of photo-consistency. The framework includes a powerful new edge preserving smoothness term and exploits the input images exhaustively, directly yielding high quality surfaces instead of dealing with issues (such as noise or misalignment) after the fact. Numeric solution is accomplished with a combination of Gauss-Newton descent and the finite element method, yielding deep convergence in few iterates. The method is fast, robust, very insensitive to view/scene configurations, and produces state-of-the-art results in the Middlebury evaluation.