Efficient Proxy Raytracer for Optical Systems using Implicit Neural Representations
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918105483051008 |
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| author | Sinaei, Shiva Zheng, Chuanjun Akşit, Kaan Iwai, Daisuke |
| author_facet | Sinaei, Shiva Zheng, Chuanjun Akşit, Kaan Iwai, Daisuke |
| contents | Ray tracing is a widely used technique for modeling optical systems, involving sequential surface-by-surface computations, which can be computationally intensive. We propose Ray2Ray, a novel method that leverages implicit neural representations to model optical systems with greater efficiency, eliminating the need for surface-by-surface computations in a single pass end-to-end model. Ray2Ray learns the mapping between rays emitted from a given source and their corresponding rays after passing through a given optical system in a physically accurate manner. We train Ray2Ray on nine off-the-shelf optical systems, achieving positional errors on the order of 1μm and angular deviations on the order 0.01 degrees in the estimated output rays. Our work highlights the potential of neural representations as a proxy for optical raytracer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Proxy Raytracer for Optical Systems using Implicit Neural Representations Sinaei, Shiva Zheng, Chuanjun Akşit, Kaan Iwai, Daisuke Machine Learning Ray tracing is a widely used technique for modeling optical systems, involving sequential surface-by-surface computations, which can be computationally intensive. We propose Ray2Ray, a novel method that leverages implicit neural representations to model optical systems with greater efficiency, eliminating the need for surface-by-surface computations in a single pass end-to-end model. Ray2Ray learns the mapping between rays emitted from a given source and their corresponding rays after passing through a given optical system in a physically accurate manner. We train Ray2Ray on nine off-the-shelf optical systems, achieving positional errors on the order of 1μm and angular deviations on the order 0.01 degrees in the estimated output rays. Our work highlights the potential of neural representations as a proxy for optical raytracer. |
| title | Efficient Proxy Raytracer for Optical Systems using Implicit Neural Representations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.20513 |