Point-Based 3D Reconstruction of Thin Objects

3D reconstruction deals with the problem of finding the shape of an object from a set of images. Thin objects that have virtually no volume pose a special challenge for reconstruction with respect to shape representation and fusion of depth information. In this paper we present a dense point-based reconstruction method that can deal with this special class of objects. We seek to jointly optimize a set of depth maps by treating each pixel as a point in space. Points are pulled towards a common surface by pairwise forces in an iterative scheme. The method also handles the problem of opposed surfaces by means of penalty forces. Efficient optimization is achieved by grouping points to superpixels and a spatial hashing approach for fast neighborhood queries. We show that the approach is on a par with state-of-the-art methods for standard multi view stereo settings and gives superior results for thin objects.