Laplacian Projection Based Global Physical Prior Smoke Reconstruction
We present a novel framework for reconstructing fluid dynamics in real-life scenarios. Our approach leverages sparse view images and incorporates physical priors across long series of frames, resulting in reconstructed fluids with enhanced physical consistency. Unlike previous methods, we utilize a...
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 12 vom: 06. Dez., Seite 7657-7671 |
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| Auteur principal: | |
| Autres auteurs: | , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
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| Accès à la collection: | IEEE transactions on visualization and computer graphics |
| Sujets: | Journal Article |
| Résumé: | We present a novel framework for reconstructing fluid dynamics in real-life scenarios. Our approach leverages sparse view images and incorporates physical priors across long series of frames, resulting in reconstructed fluids with enhanced physical consistency. Unlike previous methods, we utilize a differentiable fluid simulator (DFS) and a differentiable renderer (DR) to exploit global physical priors, reducing reconstruction errors without the need for manual regularization coefficients. We introduce divergence-free Laplacian eigenfunctions (div-free LE) as velocity bases, improving computational efficiency and memory usage. By employing gradient-related strategies, we achieve better convergence and superior results. Extensive experiments demonstrate the effectiveness of our method, showcasing improved reconstruction quality and computational efficiency compared to existing approaches. We validate our approach using both synthetic and real data, highlighting its practical potential |
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| Description: | Date Revised 28.10.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2024.3358636 |