3D Fluid Flow Estimation with Integrated Particle Reconstruction

Abstract

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: first, a per-frame reconstruction of the particles, usually in the form of soft occupancy likelihoods in a voxel representation; 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. We show, for the first time, how to jointly recon struct 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 explicitly reconstructs individual par ticles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consump tion and allows one to use the high-resolution input images for match ing. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscos ity of the fluid. The method exhibits greatly (≈70%) improved results over a recent baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of state-of-the-art tracking-based methods that require much longer sequences.