Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction

• URL: http://arxiv.org/abs/2407.09679v1
• Date: Fri, 12 Jul 2024 20:19:41 GMT
• Title: Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction
• Authors: Yiming Wang, Siyu Tang, Mengyu Chu,
• Abstract summary: Existing physics-informed neural networks 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. Building on the representation, we propose physics-informed trajectory learning and integration into NeRF-based scene reconstruction.
• Score: 17.634226193457277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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