FlameGS: Reconstruct flame light field via Gaussian Splatting
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866910765075660800 |
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| author | Shui, Yunhao Zhang, Fuhao Gao, Can Xue, Hao Ma, Zhiyin Xun, Gang Li, Xuesong |
| author_facet | Shui, Yunhao Zhang, Fuhao Gao, Can Xue, Hao Ma, Zhiyin Xun, Gang Li, Xuesong |
| contents | To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, our experimental validation shows that this model achieves an average structural similarity index of 0.96 between actual images and predicted 2D projections, along with a Peak Signal-to-Noise Ratio of 39.05. Additionally, it saves approximately 34 times the computation time and about 10 times the memory compared to traditional algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19841 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FlameGS: Reconstruct flame light field via Gaussian Splatting Shui, Yunhao Zhang, Fuhao Gao, Can Xue, Hao Ma, Zhiyin Xun, Gang Li, Xuesong Computer Vision and Pattern Recognition Image and Video Processing To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, our experimental validation shows that this model achieves an average structural similarity index of 0.96 between actual images and predicted 2D projections, along with a Peak Signal-to-Noise Ratio of 39.05. Additionally, it saves approximately 34 times the computation time and about 10 times the memory compared to traditional algorithms. |
| title | FlameGS: Reconstruct flame light field via Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2412.19841 |