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Recently, parametric mappings have emerged as highly effective surfacerepresentations, yielding low reconstruction error. In particular, the latestworks represent the target shape as an atlas of multiple mappings, which canclosely encode object parts. Atlas representations, however, suffer from onemajor drawback: The individual mappings are not guaranteed to be consistent,which results in holes in the reconstructed shape or in jagged surface areas.  We introduce an approach that explicitly encourages global consistency of thelocal mappings. To this end, we introduce two novel loss terms. The first termexploits the surface normals and requires that they remain locally consistentwhen estimated within and across the individual mappings. The second termfurther encourages better spatial configuration of the mappings by minimizingnovel stitching error. We show on standard benchmarks that the use of normalconsistency requirement outperforms the baselines quantitatively whileenforcing better stitching leads to much better visual quality of thereconstructed objects as compared to the state-of-the-art.

Better Patch Stitching for Parametric Surface Reconstruction