# Inverse Rendering Based on Compressed Spatiotemporal Infomation by Neural Networks

### Eito Itonaga, Fumihiko Sakaue, Jun Sato

#### 2023

#### Abstract

This paper proposes a method for simultaneous estimation of time variation of the light source distribution, and object shape of a target object from time-series images. This method focuses on the representational capability of neural networks, which can represent arbitrarily complex functions, and efficiently represent light source distribution, object shape, and reflection characteristics using neural networks. Using this method, we show how to stably estimate the time variation of light source distribution, and object shape simultaneously.

Download#### Paper Citation

#### in Harvard Style

Itonaga E., Sakaue F. and Sato J. (2023). **Inverse Rendering Based on Compressed Spatiotemporal Infomation by Neural Networks**. In *Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP*; ISBN 978-989-758-634-7, SciTePress, pages 467-474. DOI: 10.5220/0011792200003417

#### in Bibtex Style

@conference{visapp23,

author={Eito Itonaga and Fumihiko Sakaue and Jun Sato},

title={Inverse Rendering Based on Compressed Spatiotemporal Infomation by Neural Networks},

booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},

year={2023},

pages={467-474},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0011792200003417},

isbn={978-989-758-634-7},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP

TI - Inverse Rendering Based on Compressed Spatiotemporal Infomation by Neural Networks

SN - 978-989-758-634-7

AU - Itonaga E.

AU - Sakaue F.

AU - Sato J.

PY - 2023

SP - 467

EP - 474

DO - 10.5220/0011792200003417

PB - SciTePress