Papers › Modeling 3D Surface Manifolds with a Locally Conditioned Atlas

Modeling 3D Surface Manifolds with a Locally Conditioned Atlas

11 Feb 2021arXiv:2102.05984archive 2025-07-28

Przemysław Spurek, Sebastian Winczowski, Maciej Zięba, Tomasz Trzciński, Kacper Kania, Marcin Mazur

Recently proposed 3D object reconstruction methods represent a mesh with an atlas - a set of planar patches approximating the surface. However, their application in a real-world scenario is limited since the surfaces of reconstructed objects contain discontinuities, which degrades the quality of the final mesh. This is mainly caused by independent processing of individual patches, and in this work, we postulate to mitigate this limitation by preserving local consistency around patch vertices. To that end, we introduce a Locally Conditioned Atlas (LoCondA), a framework for representing a 3D object hierarchically in a generative model. Firstly, the model maps a point cloud of an object into a sphere. Secondly, by leveraging a spherical prior, we enforce the mapping to be locally consistent on the sphere and on the target object. This way, we can sample a mesh quad on that sphere and project it back onto the object's manifold. With LoCondA, we can produce topologically diverse objects while maintaining quads to be stitched together. We show that the proposed approach provides structurally coherent reconstructions while producing meshes of quality comparable to the competitors.

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gmum/LoCondA officialmentioned in papermentioned on GitHubpytorchMIT report

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