Iterative 3D surface modelling from a sparse set of matched feature points

We present an iterative algorithm to reconstruct a 3D object surface from a sparse set of matched feature points on the input stereo images of the object. The initial matches are sparse and do not have to be accurate. The reconstructed 3D surface is represented in terms of triangular polygons whose vertices are initially the 3D points corresponding to these matched feature points. In order to render photorealistic images of the surface, these feature points are iteratively updated. New feature points are added into the feature point set as well as the depth estimates of the feature points are refined. Experimental results showing the updated correspondences, reconstructed surfaces and virtual views rendered from new directions are presented.