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In this paper, we address the problem of inpainting in 3D digital models with large holes. The missing region inference problem is solved with a dictionary learning-based method that harnesses a geometric prior derived from a single self-similar structure and online depth databases. The underlying surface is recovered by adaptively propagating local 3D surface smoothness from around the boundary of...
While it is important to digitize heritage sites 'as is', building 3D models of damaged archaeological structures can be visually unpleasant due to the presence of large missing regions. This work addresses intensity filling-in, or intensity inpainting, of such large damaged regions post geometric reconstruction. Assuming a Lambertian image formation model, we first establish that patches corresponding...
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...
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