Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems

Ziyuan Luo; Boxin Shi; Haoliang Li; Renjie Wan

doi:10.1007/978-3-031-72667-5_20

Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems

Ziyuan Luo, Boxin Shi, Haoliang Li, Renjie Wan^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Electromagnetic Inverse Scattering Problems (EISP) have gained wide applications in computational imaging. By solving EISP, the internal relative permittivity of the scatterer can be non-invasively determined based on the scattered electromagnetic fields. Despite previous efforts to address EISP, achieving better solutions to this problem has remained elusive, due to the challenges posed by inversion and discretization. This paper tackles those challenges in EISP via an implicit approach. By representing the scatterer’s relative permittivity as a continuous implicit representation, our method is able to address the low-resolution problems arising from discretization. Further, optimizing this implicit representation within a forward framework allows us to conveniently circumvent the challenges posed by inverse estimation. Our approach outperforms existing methods on standard benchmark datasets. Project page: https://luo-ziyuan.github.io/Imaging-Interiors.

Original language	English
Title of host publication	Computer Vision – ECCV 2024
Subtitle of host publication	18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII
Editors	Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
Publisher	Springer Cham
Pages	350–368
Number of pages	19
Edition	1st
ISBN (Electronic)	9783031726675
ISBN (Print)	9783031726668
DOIs	https://doi.org/10.1007/978-3-031-72667-5_20
Publication status	Published - 28 Sept 2024
Event	18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024 https://eccv.ecva.net/Conferences/2024 (Conference Website) https://link.springer.com/book/10.1007/978-3-031-73232-4 (Conference Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Volume	15065
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	ECCV: European Conference on Computer Vision

Conference

Conference	18th European Conference on Computer Vision, ECCV 2024
Country/Territory	Italy
City	Milan
Period	29/09/24 → 4/10/24
Internet address	https://eccv.ecva.net/Conferences/2024 (Conference Website) https://link.springer.com/book/10.1007/978-3-031-73232-4 (Conference Proceedings)

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

User-Defined Keywords

Electromagnetic inverse scattering problems
Implicit neural representations
Computational photography

Access to Document

10.1007/978-3-031-72667-5_20

Cite this

Luo, Z., Shi, B., Li, H., & Wan, R. (2024). Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII (1st ed., pp. 350–368). (Lecture Notes in Computer Science; Vol. 15065), (ECCV: European Conference on Computer Vision). Springer Cham. https://doi.org/10.1007/978-3-031-72667-5_20

Luo, Ziyuan ; Shi, Boxin ; Li, Haoliang et al. / Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems. Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII. editor / Aleš Leonardis ; Elisa Ricci ; Stefan Roth ; Olga Russakovsky ; Torsten Sattler ; Gül Varol. 1st. ed. Springer Cham, 2024. pp. 350–368 (Lecture Notes in Computer Science). (ECCV: European Conference on Computer Vision).

@inproceedings{f4069f7236944edb9a21b6710b03735c,

title = "Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems",

abstract = "Electromagnetic Inverse Scattering Problems (EISP) have gained wide applications in computational imaging. By solving EISP, the internal relative permittivity of the scatterer can be non-invasively determined based on the scattered electromagnetic fields. Despite previous efforts to address EISP, achieving better solutions to this problem has remained elusive, due to the challenges posed by inversion and discretization. This paper tackles those challenges in EISP via an implicit approach. By representing the scatterer{\textquoteright}s relative permittivity as a continuous implicit representation, our method is able to address the low-resolution problems arising from discretization. Further, optimizing this implicit representation within a forward framework allows us to conveniently circumvent the challenges posed by inverse estimation. Our approach outperforms existing methods on standard benchmark datasets. Project page: https://luo-ziyuan.github.io/Imaging-Interiors.",

keywords = "Electromagnetic inverse scattering problems, Implicit neural representations, Computational photography",

author = "Ziyuan Luo and Boxin Shi and Haoliang Li and Renjie Wan",

note = "This work was done at Renjie{\textquoteright}s Research Group at the Department of Computer Science of Hong Kong Baptist University. Renjie{\textquoteright}s Research Group is supported by the National Natural Science Foundation of China under Grant No. 62302415, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2022A1515110692, 2024A1515012822, and the Blue Sky Research Fund of HKBU under Grant No. BSRF/21-22/16. Boxin Shi is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102. Haoliang Li is supported by Changsha Technology Fund under its grant No. KH2304007. Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",

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booktitle = "Computer Vision – ECCV 2024",

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Luo, Z, Shi, B, Li, H & Wan, R 2024, Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems. in A Leonardis, E Ricci, S Roth, O Russakovsky, T Sattler & G Varol (eds), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII. 1st edn, Lecture Notes in Computer Science, vol. 15065, ECCV: European Conference on Computer Vision, Springer Cham, pp. 350–368, 18th European Conference on Computer Vision, ECCV 2024, Milan, Italy, 29/09/24. https://doi.org/10.1007/978-3-031-72667-5_20

Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems. / Luo, Ziyuan; Shi, Boxin; Li, Haoliang et al.
Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII. ed. / Aleš Leonardis; Elisa Ricci; Stefan Roth; Olga Russakovsky; Torsten Sattler; Gül Varol. 1st. ed. Springer Cham, 2024. p. 350–368 (Lecture Notes in Computer Science; Vol. 15065), (ECCV: European Conference on Computer Vision).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems

AU - Luo, Ziyuan

AU - Shi, Boxin

AU - Li, Haoliang

AU - Wan, Renjie

N1 - This work was done at Renjie’s Research Group at the Department of Computer Science of Hong Kong Baptist University. Renjie’s Research Group is supported by the National Natural Science Foundation of China under Grant No. 62302415, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2022A1515110692, 2024A1515012822, and the Blue Sky Research Fund of HKBU under Grant No. BSRF/21-22/16. Boxin Shi is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102. Haoliang Li is supported by Changsha Technology Fund under its grant No. KH2304007. Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2024/9/28

Y1 - 2024/9/28

N2 - Electromagnetic Inverse Scattering Problems (EISP) have gained wide applications in computational imaging. By solving EISP, the internal relative permittivity of the scatterer can be non-invasively determined based on the scattered electromagnetic fields. Despite previous efforts to address EISP, achieving better solutions to this problem has remained elusive, due to the challenges posed by inversion and discretization. This paper tackles those challenges in EISP via an implicit approach. By representing the scatterer’s relative permittivity as a continuous implicit representation, our method is able to address the low-resolution problems arising from discretization. Further, optimizing this implicit representation within a forward framework allows us to conveniently circumvent the challenges posed by inverse estimation. Our approach outperforms existing methods on standard benchmark datasets. Project page: https://luo-ziyuan.github.io/Imaging-Interiors.

AB - Electromagnetic Inverse Scattering Problems (EISP) have gained wide applications in computational imaging. By solving EISP, the internal relative permittivity of the scatterer can be non-invasively determined based on the scattered electromagnetic fields. Despite previous efforts to address EISP, achieving better solutions to this problem has remained elusive, due to the challenges posed by inversion and discretization. This paper tackles those challenges in EISP via an implicit approach. By representing the scatterer’s relative permittivity as a continuous implicit representation, our method is able to address the low-resolution problems arising from discretization. Further, optimizing this implicit representation within a forward framework allows us to conveniently circumvent the challenges posed by inverse estimation. Our approach outperforms existing methods on standard benchmark datasets. Project page: https://luo-ziyuan.github.io/Imaging-Interiors.

KW - Electromagnetic inverse scattering problems

KW - Implicit neural representations

KW - Computational photography

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U2 - 10.1007/978-3-031-72667-5_20

DO - 10.1007/978-3-031-72667-5_20

M3 - Conference proceeding

SN - 9783031726668

T3 - Lecture Notes in Computer Science

SP - 350

EP - 368

BT - Computer Vision – ECCV 2024

A2 - Leonardis, Aleš

A2 - Ricci, Elisa

A2 - Roth, Stefan

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Luo Z, Shi B, Li H, Wan R. Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems. In Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors, Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part VII. 1st ed. Springer Cham. 2024. p. 350–368. (Lecture Notes in Computer Science). (ECCV: European Conference on Computer Vision). doi: 10.1007/978-3-031-72667-5_20

Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems

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