ProJo4D uses progressive joint optimization to solve sparse-view inverse physics estimation, outperforming prior methods with up to 10x better geometric accuracy in 4D state prediction and material estimation.
Physics informed neural fields for smoke reconstruction with sparse data
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ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
ProJo4D uses progressive joint optimization to solve sparse-view inverse physics estimation, outperforming prior methods with up to 10x better geometric accuracy in 4D state prediction and material estimation.