Dense Motion Estimation for Smoke
This addresses a specific computer vision problem for applications like fluid dynamics analysis, though it appears incremental as it builds on existing dense motion estimation methods.
The paper tackles the problem of dense motion estimation for highly dynamic smoke phenomena, which is challenging due to non-rigid and large motions. The result is a robust and fast algorithm that outperforms state-of-the-art and neural network approaches on various smoke types.
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.