  We present a novel geometric deep learning layer that leverages the varifold
gradient (VariGrad) to compute feature vector representations of 3D geometric
data. These feature vectors can be used in a variety of downstream learning
tasks such as classification, registration, and shape reconstruction. Our
model's use of parameterization independent varifold representations of
geometric data allows our model to be both trained and tested on data
independent of the given sampling or parameterization. We demonstrate the
efficiency, generalizability, and robustness to resampling demonstrated by the
proposed VariGrad layer.
