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We present a novel geometric deep learning method to compute the acousticscattering properties of geometric objects. Our learning algorithm uses a pointcloud representation of objects to compute the scattering properties andintegrates them with ray tracing for interactive sound propagation in dynamicscenes. We use discrete Laplacian-based surface encoders and approximate theneighborhood of each point using a shared multi-layer perceptron. We show thatour formulation is permutation invariant and present a neural network thatcomputes the scattering function using spherical harmonics. Our approach canhandle objects with arbitrary topologies and deforming models, and takes lessthan 1ms per object on a commodity GPU. We have analyzed the accuracy andperform validation on thousands of unseen 3D objects and highlight the benefitsover other point-based geometric deep learning methods. To the best of ourknowledge, this is the first real-time learning algorithm that can approximatethe acoustic scattering properties of arbitrary objects with high accuracy.

Point-based Acoustic Scattering for Interactive Sound Propagation via Surface Encoding