Neural Refractive Index Primitives for Flame Field Reconstruction Using Background-Oriented Schlieren
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911683097657344 |
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| author | Lu, Xinyi Hu, Wei Liao, Zizhou Wang, Zheng Zhang, Yue Li, Jingxuan |
| author_facet | Lu, Xinyi Hu, Wei Liao, Zizhou Wang, Zheng Zhang, Yue Li, Jingxuan |
| contents | An improved neural refractive-index-primitive method for background-oriented schlieren tomography is presented, enabling continuous three-dimensional reconstruction of refractive-index fields using a compact multilayer perceptron. The method adopts the refractive-index field as the sole neural primitive and integrates multiresolution hash encoding, automatic-discrete gradient losses, and a three-dimensional mask to enable fast convergence and high-resolution, spatially coherent reconstructions. Tests on numerical combustion phantoms and real flame data demonstrate accurate recovery of both large-scale structures and fine-scale turbulence, strong robustness to noise, and clear advantages over frequency-encoding-based and voxel-based reconstruction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11454 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Refractive Index Primitives for Flame Field Reconstruction Using Background-Oriented Schlieren Lu, Xinyi Hu, Wei Liao, Zizhou Wang, Zheng Zhang, Yue Li, Jingxuan Fluid Dynamics An improved neural refractive-index-primitive method for background-oriented schlieren tomography is presented, enabling continuous three-dimensional reconstruction of refractive-index fields using a compact multilayer perceptron. The method adopts the refractive-index field as the sole neural primitive and integrates multiresolution hash encoding, automatic-discrete gradient losses, and a three-dimensional mask to enable fast convergence and high-resolution, spatially coherent reconstructions. Tests on numerical combustion phantoms and real flame data demonstrate accurate recovery of both large-scale structures and fine-scale turbulence, strong robustness to noise, and clear advantages over frequency-encoding-based and voxel-based reconstruction methods. |
| title | Neural Refractive Index Primitives for Flame Field Reconstruction Using Background-Oriented Schlieren |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2605.11454 |