Laplacian Projection Based Global Physical Prior Smoke Reconstruction

Xiao, Shibang; Tong, Chao; Zhang, Qifan; Cen, Yunchi; Li, Frederick W. B.; Liang, Xiaohui

Abstract

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.

Citation

Xiao, S., Tong, C., Zhang, Q., Cen, Y., Li, F. W. B., & Liang, X. (2024). Laplacian Projection Based Global Physical Prior Smoke Reconstruction. IEEE Transactions on Visualization and Computer Graphics, 30(12), 7657-7671. https://doi.org/10.1109/tvcg.2024.3358636

• Journal Article Type: Article
• Acceptance Date: Jan 22, 2024
• Online Publication Date: Jan 25, 2024
• Publication Date: 2024-12
• Deposit Date: Apr 29, 2024
• Publicly Available Date: May 1, 2024
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Print ISSN: 1077-2626
• Electronic ISSN: 1941-0506
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Issue: 12
• Pages: 7657-7671
• DOI: https://doi.org/10.1109/tvcg.2024.3358636
• Public URL: https://durham-repository.worktribe.com/output/2407995