---
title: "Point-based Acoustic Scattering for Interactive Sound Propagation via Surface Encoding"
authors:
  - "Hsien-Yu Meng"
  - "Zhenyu Tang"
  - "Dinesh Manocha"
doi: "10.24963/ijcai.2021/126"
url: "https://ijcai.org/proceedings/2021/126"
pdf: "https://ijcai.org/proceedings/2021/0126.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "909-915"
track: "Main Track"
keywords:
  - "Computer Vision: 2D and 3D Computer Vision"
  - "Multidisciplinary Topics and Applications: Interactive Entertainment"
---

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

Hsien-Yu Meng, Zhenyu Tang, Dinesh Manocha

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 909-915. DOI: [10.24963/ijcai.2021/126](https://doi.org/10.24963/ijcai.2021/126)

## Abstract

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.