Pregled bibliografske jedinice broj: 1260162
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering // Proceedings of the 2022 56th Asilomar Conference on Signals, Systems, and Computers
Pacific Grove (CA), Sjedinjene Američke Države, 2022. str. 947-952 doi:10.1109/IEEECONF56349.2022.10052055 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1260162 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering
Autori
Vlašić, Tin ; Nguyen, Hieu ; Khorashadizadeh, AmirEhsan ; Dokmanić, Ivan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 2022 56th Asilomar Conference on Signals, Systems, and Computers
/ - , 2022, 947-952
Skup
2022 56th Asilomar Conference on Signals, Systems, and Computers
Mjesto i datum
Pacific Grove (CA), Sjedinjene Američke Države, 31.10.2022. - 02.11.2022
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Helmholtz equation ; implicit boundary integral method ; inverse scattering problem ; signed distance function
Sažetak
Implicit representation of shapes as level sets of multilayer perceptrons has recently flourished in different shape analysis, compression, and reconstruction tasks. In this paper, we introduce an implicit neural representation-based framework for solving the inverse obstacle scattering problem in a mesh-free fashion. We express the obstacle shape as the zero-level set of a signed distance function which is implicitly determined by network parameters. To solve the direct scattering problem, we implement the implicit boundary integral method. It uses projections of the grid points in the tubular neighborhood onto the boundary to compute the PDE solution directly in the level-set framework. The proposed implicit representation conveniently handles the shape perturbation in the optimization process. To update the shape, we use PyTorch's automatic differentiation to backpropagate the loss function w.r.t. the network parameters, allowing us to avoid complex and error-prone manual derivation of the shape derivative. Additionally, we propose a deep generative model of implicit neural shape representations that can fit into the framework. The deep generative model effectively regularizes the inverse obstacle scattering problem, making it more tractable and robust, while yielding high-quality reconstruction results even in noise-corrupted setups.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
HRZZ-IP-2019-04-6703 - Renesansa teorije uzorkovanja (SamplingRenaissance) (Seršić, Damir, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
