Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction
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Date
2024-07
Publication Type
Conference Paper
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yes
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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.
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published
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Book title
SIGGRAPH '24: ACM SIGGRAPH 2024 Conference Papers
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Volume
Pages / Article No.
53
Publisher
Association for Computing Machinery
Event
ACM SIGGRAPH 2024 Conference (SIGGRAPH 2024)
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Subject
Fluid Reconstruction; Physics-Informed Deep Learning; NeRF