3D Fluid Flow Estimation with Integrated Particle Reconstruction

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps, utilizing either a pure Eulerian or pure Lagrangian approach. Eulerian methods perform a voxel-based reconstruction of particles per time step, followed by 3D motion estimation, with some form of dense matching between the precomputed voxel grids from different time steps. In this sequential procedure, the first step cannot use temporal consistency considerations to support the reconstruction, while the second step has no access to the original, high-resolution image data. Alternatively, Lagrangian methods reconstruct an explicit, sparse set of particles and track the individual particles over time. Physical constraints can only be incorporated in a post-processing step when interpolating the particle tracks to a dense motion field. We show, for the first time, how to jointly reconstruct both the individual tracer particles and a dense 3D fluid motion field from the image data, using an integrated energy minimization. Our hybrid Lagrangian/Eulerian model reconstructs individual particles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consumption and allows one to use the high-res input images for matching. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscosity of the fluid. The method exhibits greatly (~70%) improved results over our recently published baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of sota tracking-based methods that require much longer sequences.Comment: To appear in International Journal of Computer Vision (IJCV

EFFICIENT PARTICLE-BASED VISCOUS FLUID SIMULATION WITH VIDEO-GUIDED REAL-TO-VIRTUAL PARAMETER TRANSFER

Viscous fluids, such as honey and molten chocolate, are common materials frequently seen in our daily life. These viscous fluids exhibit characteristic behaviors. Capturing and understanding such dynamics have been required for various applications. Although recent research made advances in simulating the viscous fluid dynamics, still many challenges are left to be addressed. In this dissertation, I present novel techniques to more efficiently and accurately simulate viscous fluid dynamics and propose a parameter identification framework to facilitate the tedious parameter tuning steps for viscous materials. In fluid simulation, enforcing the incompressibility robustly and efficiently is essential. One known challenge is how to set appropriate boundary conditions for free surfaces and solid boundaries. I propose a new boundary handling approach for an incompressible particle-based solver based on the connectivity analysis for simulation particles. Another challenge is that previously proposed techniques do not scale well. To address this, I propose a new multilevel particle-based solver which constructs the hierarchy of simulation particles. These techniques improve the robustness and efficiency achieving the nearly linear scaling unlike previous approaches. To simulate characteristic behaviors of viscous fluids, such as coiling and buckling phenomena and adhesion to other materials, it is necessary to develop a specialized solver. I propose a stable and efficient particle-based solver for simulating highly viscous fluids by using implicit integration with the full form of viscosity. To simulate more accurate interactions with solid objects, I propose a new two-way fluid-solid coupling method for viscous fluids via the unified minimization. These approaches also improve the robustness and efficiency while generating rotational and sticky behaviors of viscous fluids. One important challenge for the physically-based simulation is that it is not obvious how to choose appropriate material parameters to generate our desirable behaviors of simulated materials. I propose a parameter identification framework that helps to tune material parameters for viscous fluids with example video data captured from real world fluid phenomena. This framework identifies viscosity parameters for the real viscous fluids while estimating the hidden variables for the fluids, and enables the parameter transfer from the real world to virtual environment.Doctor of Philosoph