Fluid surface reconstruction based on specular reflection model

Yu, Mingqi; Quan, Hongyan

doi:10.1002/cav.1526

Cited by 13 publications

(18 citation statements)

References 27 publications

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“…Pickup et al [1][2][3] make significant contribution to surface reconstruction from illumination property and reduce the cost of complex computation in fluid reconstruction. To reconstruct accurately fluid surface in real time, Yu et al [4,5] advance the current state of the art and propose a linear solution for it.…”

Section: Related Workmentioning

confidence: 99%

“…Their results are probably not accurate enough and unfit for the driven model recovery. Unlike the previous work based on the normalized geometry [4], we introduce the scale factor S between normalized geometry and non-normalized geometry and further calculate it in the multi-scale framework. Moreover, in the application of obtaining larger or launching a longer desired result, just surface geometry is not enough, and physically based driven model is promising.…”

Section: Introductionmentioning

confidence: 99%

“…They made a good balance between the visual plausibility and the production cost, and provided a video-based method to reconstruct the fluid surface. Yu et al [4,5] presented the current state of art and proposed an approach based on Phong model to obtain fluid surface geometry in real time linearly. In these studies, either the fluid velocity is estimated from the optimized framework [3], or surface geometry is calculated in normalized condition [4].…”

Section: Introductionmentioning

confidence: 99%

“…Yu et al [4,5] presented the current state of art and proposed an approach based on Phong model to obtain fluid surface geometry in real time linearly. In these studies, either the fluid velocity is estimated from the optimized framework [3], or surface geometry is calculated in normalized condition [4]. Their results are probably not accurate enough and unfit for the driven model recovery.…”

Section: Introductionmentioning

confidence: 99%

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Fluid re-simulation based on physically driven model from video

2015

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Customizing a desired naturalistic fluid simulation result from video to obtain similar artistic effect is significant in practice. But art-directed customizing is challengeable due to the chaotic nature of the physics contained in it, and this still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. This paper focuses on the problem of physically based fluid re-simulation which is foundational to customize desired naturalistic simulation result from video example. In the previous achievements, conventional algorithms primarily recover 3D geometry of fluid surface or obtain the velocity of fluid particles in video. However, to launch new derivative results, just geometry and velocity are not enough, and physically based driven models are promising. We present a novel method that is capable of efficiently recovering physically driven model from an existing video. We advocate a new approach to calculate the velocity and non-normalized surface geometry under the constraints of the appearance and dynamic behavior of the example fluid in multi-scale framework, and use the calculated physical properties quickly to recover the fluid-driven model. In particular, to calculate the surface geometry more accurately, we introduce a scale factor between normalized geometry and non-normalized geometry to acquire a more accurate result. We propose a novel recovery algorithm, in which the particle densities of lattice Boltzmann method can be recovered more accurately from matching fluid advection geometry with the calculated non-normalized geometry. Fluid re-simulations with different types of density can be achieved, including constrained particle density, auto-advection density, and enhanced particle density. We demonstrate our results in several challenging scenarios and provide qualitative evaluation to our method. Some applications based on our approach are also demonstrated in the implementation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fluid re-simulation based on physically driven model from video

2015

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“…Figure 1 shows the pipeline of our 3D realistic re-simulation algorithm. The detail is as follows: after acquiring geometry from SFS [8], the velocity is calculated from dynamic behavior constrained by SWE, particle density and distribution function in LBMSW model are computed under the constraint of the surface geometry and the velocity. The contrast of density is enhanced to make up its decaying in the auto-advection.…”

Section: Introductionmentioning

confidence: 99%

Enhancement in Realistic Fluid Re-simulation

Song¹,

Quan²,

Huang³

2016

Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

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Abstract-Re-simulating a 3D fluid result from video while retaining and rendering details is significant in practice, which still remains a difficult task in spite of rapid advancements in this field during the last two decades. Physically driven models can be easily extended to handle fluid, yet they are unable to preserve surface details like breaking waves without the expense of increasing particle densities. This paper proposes a hybrid particle Lattice Boltzmann Model for Shallow Waters (LBMSW) coupling method to simulate fluid with finer details real-time from video. To preserve re-simulation surface details, we couple the detail particles with the distribution function of the LBMSW model. To improve the time performance, we use only surface to re-simulate. Experiments show that the proposed approach can obtain a realistic re-simulation products from video example in several challenging scenarios and we provide qualitative evaluation to the method.

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Multitask learning on monocular water images: Surface reconstruction and image synthesis

Xie

Zhai

Hou

et al. 2019

Computer Animation & Virtual

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In this paper, we present a new strategy, a joint deep learning architecture, for two classic tasks in computer graphics: water surface reconstruction and water image synthesis. Modeling water surfaces from single images can be regarded as the inverse of image rendering, which converts surface geometries into photorealistic images. On the basis of this fact, we therefore consider these two problems as a cycle image‐to‐image translation and propose to tackle them together using a pair of neural networks, with the three‐dimensional surface geometries being represented as two‐dimensional surface normal maps. Furthermore, we also estimate the imaging parameters from the existing water images with a subnetwork to reuse the lighting conditions when synthesizing new images. Experiments demonstrate that our method achieves an accurate reconstruction of surfaces from monocular images efficiently and produces visually plausible new images under variable lighting conditions.

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Fluid surface reconstruction based on specular reflection model

Cited by 13 publications

References 27 publications

Fluid re-simulation based on physically driven model from video

Fluid re-simulation based on physically driven model from video

Enhancement in Realistic Fluid Re-simulation

Multitask learning on monocular water images: Surface reconstruction and image synthesis

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