Harnessing self-similarity for reconstruction of large missing regions in 3D Models

Dense point clouds of naturally existing but damaged real world structures result in 3D models that may be visually unpleasant for applications such as a `Virtual Tour'. This paper addresses the reconstruction of such damaged regions (or, `holes') in digital models. Without constraining the complexity or size of the hole, a noniterative framework based on Tensor Voting (TV) is proposed to fill-in missing regions by using neighbourhood surface geometry and geometric prior derived from registered self-similar examples. The effectiveness of the reconstruction is demonstrated on holes with different complexities on synthetic and real data.