10 citations · 18 across the 30 of their papers we have counts for
4 papers · 2 filters
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
Richard Liu, Noam Aigerman, Vladimir G. Kim +1
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by in…
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Bo Sun, Vladimir G. Kim, Noam Aigerman +2
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative meth…
Neural Convolutional Surfaces
Luca Morreale, Noam Aigerman, Paul Guerrero +2
This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement…
Möbius Convolutions for Spherical CNNs
Thomas W. Mitchel, Noam Aigerman, Vladimir G. Kim +1
Möbius transformations play an important role in both geometry and spherical image processing - they are the group of conformal automorphisms of 2D surfaces and the spherical equiv…