Dense Motion Estimation for Smoke

TL;DR

This paper introduces a robust and fast dense motion estimation algorithm specifically designed for smoke, leveraging skeletal flow to improve accuracy over existing methods, including neural network approaches.

Contribution

The paper presents a novel skeletal flow-based algorithm that estimates dense smoke motion without explicit point matching, outperforming existing methods.

Findings

Better performance over different smoke types

Robustness to non-rigid and large motions

Outperforms state-of-the-art algorithms

Abstract

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.