Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction
<|reference_start|>Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction: We delve into the physics-informed neural reconstruction of smoke and obstacles through sparse-view RGB videos, tackling challenges arising from limited observation of complex dynamics. Existing physics-informed neural networks often emphasize short-term physics constraints, leaving the proper preservation of long-term conservation less explored. We introduce Neural Characteristic Trajectory Fields, a novel representation utilizing Eulerian neural fields to implicitly model Lagrangian fluid trajectories. This topology-free, auto-differentiable representation facilitates efficient flow map calculations between arbitrary frames as well as efficient velocity extraction via auto-differentiation. Consequently, it enables end-to-end supervision covering long-term conservation and short-term physics priors. Building on the representation, we propose physics-informed trajectory learning and integration into NeRF-based scene reconstruction. We enable advanced obstacle handling through self-supervised scene decomposition and seamless integrated boundary constraints. Our results showcase the ability to overcome challenges like occlusion uncertainty, density-color ambiguity, and static-dynamic entanglements. Code and sample tests are at \url{https://github.com/19reborn/PICT_smoke}.<|reference_end|>
@article{wang2024physics-informed,
    title={Physics-Informed Learning of Characteristic Trajectories for Smoke
  Reconstruction},
    author={Yiming Wang, Siyu Tang, Mengyu Chu},
    journal={arXiv preprint arXiv:2407.09679},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09679},
    primaryClass={cs.CV cs.GR cs.LG}
}