Abstract

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 across different categories share similarities, which leads to more generalizable models. We then introduce a novel method to learn this patch-based representation in a canonical space, such that it is as object-agnostic as possible. We show that our representation trained on one category of objects from ShapeNet can also well represent detailed shapes from any other category. In addition, it can be trained using much fewer shapes, compared to existing approaches. We show several applications of our new representation, including shape interpolation and partial point cloud completion. Due to explicit control over positions, orientations and scales of patches, our representation is also more controllable compared to object-level representations, which enables us to deform encoded shapes non-rigidly.

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Citation

BibTeX, 1 KB

@article{Tretschk2020PatchNets, 
       author = {Tretschk, Edgar and Tewari, Ayush and Golyanik, Vladislav and Zollh\"{o}fer, Michael and Stoll, Carsten and Theobalt, Christian}, 
        title = "{PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations}", 
      journal = {European Conference on Computer Vision (ECCV)}, 
         year = "2020" 
} 
				

Acknowledgments

This work was supported by the ERC Consolidator Grant 4DRepLy (770784), and an Oculus research grant.

Contact

For questions, clarifications, please get in touch with:
Edgar Tretschk tretschk@mpi-inf.mpg.de
Vladislav Golyanik golyanik@mpi-inf.mpg.de

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