Physics-driven Fire Modeling from Multi-view Images
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2018
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| _version_ | 1866916009954246656 |
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| author | Dorta, Gara Benedetti, Luca Kit, Dmitry Yang, Yong-Liang |
| author_facet | Dorta, Gara Benedetti, Luca Kit, Dmitry Yang, Yong-Liang |
| contents | Fire effects are widely used in various computer graphics applications such as visual effects and video games. Modeling the shape and appearance of fire phenomenon is challenging as the underlying effects are driven by complex laws of physics. State-of-the-art fire modeling techniques rely on sophisticated physical simulations which require intensive parameter tuning, or use simplifications which produce physically invalid results. In this paper, we present a novel method of reconstructing physically valid fire models from multi-view stereo images. Our method, for the first time, provides plausible estimation of physical properties (e.g., temperature, density) of a fire volume using RGB cameras. This allows for a number of novel phenomena such as global fire illumination effects. The effectiveness and usefulness of our method are tested by generating fire models from a variety of input data, and applying the reconstructed fire models for realistic illumination of virtual scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1804_05261 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Physics-driven Fire Modeling from Multi-view Images Dorta, Gara Benedetti, Luca Kit, Dmitry Yang, Yong-Liang Computer Vision and Pattern Recognition Graphics Fire effects are widely used in various computer graphics applications such as visual effects and video games. Modeling the shape and appearance of fire phenomenon is challenging as the underlying effects are driven by complex laws of physics. State-of-the-art fire modeling techniques rely on sophisticated physical simulations which require intensive parameter tuning, or use simplifications which produce physically invalid results. In this paper, we present a novel method of reconstructing physically valid fire models from multi-view stereo images. Our method, for the first time, provides plausible estimation of physical properties (e.g., temperature, density) of a fire volume using RGB cameras. This allows for a number of novel phenomena such as global fire illumination effects. The effectiveness and usefulness of our method are tested by generating fire models from a variety of input data, and applying the reconstructed fire models for realistic illumination of virtual scenes. |
| title | Physics-driven Fire Modeling from Multi-view Images |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/1804.05261 |