11 citations · 12 across the 4 of their papers we have counts for
6 papers
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
Noam Aigerman, Kunal Gupta, Vladimir G. Kim +3
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogen…
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…
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri +4
We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically me…
Field Convolutions for Surface CNNs
Thomas W. Mitchel, Vladimir G. Kim, Michael Kazhdan
We present a novel surface convolution operator acting on vector fields that is based on a simple observation: instead of combining neighboring features with respect to a single co…
Neural Surface Maps
Luca Morreale, Noam Aigerman, Vladimir Kim +1
Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. Accordingly, in geometry processing, maps are ubi…
FAN: Focused Attention Networks
Chu Wang, Babak Samari, Vladimir Kim +2
Attention networks show promise for both vision and language tasks, by emphasizing relationships between constituent elements through weighting functions. Such elements could be re…