This paper puts forward a hierarchical method of fluid surface modeling in natural landscapes. The proposed method produces a visually plausible surface geometry with the texture from a single video image recorded by a standard video device. In contrast with the conventional physically based fluid simulation, our method computes preliminary results using empirical method and adopts Stokes wave model to obtain the reconstruction result. We illustrate the working of system with a wide range of possible scene, and a qualitative evaluation of our method is provided to verify the quality of the surface geometry. The experiment shows that the method can meet the requirement of real-time performance and the reality of the fluid.
Realistic fluid scene modeling is necessary for virtual reality application. Large 3D fluid scene modeling in low performance computer with real time remains a challenge. Here we present an approach for synthesizing large 3D fluid scene with example of frame in video. Both rich of realistic texture in video frame and height field of fluid surface are employed to study. Realistic textures can enhance the synthesized fluid appearance, whereas the height field of fluid surface enables the generation of complex geometry and stochastic movement on the surface. We take advantage of fluid wave theory to study and extract wave elements from fluid surface of example frame. The extracted wave elements are clustered and rearranged into the synthesized result. MST (Minimum Spanning Tree) of wave element classes is constituted to keep local continuity to fluid surface. We demonstrate our synthesis results for different scales and different types of large 3D fluid scenes synthesis in several challenging scenarios.
This paper puts forward a new method of realtime reconstruction of fluid in natural scene. It takes the measure of combination of image analysis and LBM (Lattice Boltzmann Methods). First, employs LK (Lucas-Kanade) method to calculate the dense optical flow, and then takes LBM to obtain the joint force of central particles for the initial result. After backfilling the velocity vectors field, it adopts the K-means cluster to obtain several classes, in each class, it takes advantage of the Rayleigh distribution to fit the height field of fluid. Finally, the reconstruction result of fluid is obtained. In addition, it demonstrates the results of the height field of fluid in the experiment. Further experiments shows that it is a valid method of fluid reconstruction with real time and can be used in the study of natural landscape fluid with efficiency and feasibility.
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