{"type":"ScholarlyArticle","title":"Point-based Acoustic Scattering for Interactive Sound Propagation via Surface Encoding","authors":[{"name":"Hsien-Yu Meng","slug":"hsien-yu-meng","given":"Hsien-Yu","family":"Meng","affiliation":""},{"name":"Zhenyu Tang","slug":"zhenyu-tang","given":"Zhenyu","family":"Tang","affiliation":""},{"name":"Dinesh Manocha","slug":"dinesh-manocha","given":"Dinesh","family":"Manocha","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/126","url":"https://ijcai.org/proceedings/2021/126","pdf":"https://ijcai.org/proceedings/2021/0126.pdf","markdown":"https://ijcai.org/proceedings/2021/126.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/126","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Computer Vision I","track":"Main Track","number":126,"pages":{"first":909,"last":915},"keywords":["Computer Vision: 2D and 3D Computer Vision","Multidisciplinary Topics and Applications: Interactive Entertainment"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}