  We present a novel geometric deep learning method to compute the acoustic
scattering properties of geometric objects. Our learning algorithm uses a point
cloud representation of objects to compute the scattering properties and
integrates them with ray tracing for interactive sound propagation in dynamic
scenes. We use discrete Laplacian-based surface encoders and approximate the
neighborhood of each point using a shared multi-layer perceptron. We show that
our formulation is permutation invariant and present a neural network that
computes the scattering function using spherical harmonics. Our approach can
handle objects with arbitrary topologies and deforming models, and takes less
than 1ms per object on a commodity GPU. We have analyzed the accuracy and
perform validation on thousands of unseen 3D objects and highlight the benefits
over other point-based geometric deep learning methods. To the best of our
knowledge, this is the first real-time learning algorithm that can approximate
the acoustic scattering properties of arbitrary objects with high accuracy.
