Coupled Fluid Density and Motion from Single Views

TL;DR

This paper introduces a method to reconstruct 3D fluid density and motion from a single image sequence by leveraging physical priors, enabling simpler setups and applications like re-simulation and domain modification.

Contribution

The authors propose a novel approach that couples density and motion inference from single views using physical priors, discretization, and regularizers, advancing fluid reconstruction techniques.

Findings

Successful reconstruction of synthetic fluid flows.

Reconstruction of real smoke plumes from Raspberry Pi videos.

Method enables potential online and mobile applications.

Abstract

We present a novel method to reconstruct a fluid's 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under-determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth-based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart-phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re-simulation, domain modification or guiding purposes. We will demonstrate the capacity of our method with a series of synthetic test cases and the reconstruction of real smoke plumes captured with a Raspberry Pi camera.