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- W3035605473 abstract "Recovering the dynamic fluid surface is a long-standing challenging problem in computer vision. Most existing image-based methods require multiple views or a dedicated imaging system. Here we present a learning-based single-image approach for 3D fluid surface reconstruction. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background image. Due to the dynamic nature of fluid surfaces, our network uses recurrent layers that carry temporal information from previous frames to achieve spatio-temporally consistent reconstruction given a video input. Due to the lack of fluid data, we synthesize a large fluid dataset using physics-based fluid modeling and rendering techniques for network training and validation. Through experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural network trained on our fluid dataset can recover dynamic 3D fluid surfaces with high accuracy." @default.
- W3035605473 created "2020-06-19" @default.
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- W3035605473 date "2020-06-01" @default.
- W3035605473 modified "2023-09-28" @default.
- W3035605473 title "Dynamic Fluid Surface Reconstruction Using Deep Neural Network" @default.
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- W3035605473 doi "https://doi.org/10.1109/cvpr42600.2020.00010" @default.
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