PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations

@inproceedings{Tretschk2020PatchNetsPG,
  title={PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations},
  author={Edgar Tretschk and Ayush Tewari and Vladislav Golyanik and Michael Zollh{\"o}fer and Carsten Stoll and Christian Theobalt},
  booktitle={European Conference on Computer Vision},
  year={2020}
}
Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with arbitrary topology. Since a continuous function is learned, the reconstructions can also be extracted at any arbitrary resolution. However, large datasets such as ShapeNet are required to train such models. In this paper, we present a new mid-level patch-based surface representation. At the level of patches, objects… 

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