Dense Matchers for Dense Tracking
Tomáš Jelínek Jonáš Šerých Jiří Matas
Optical flow is a useful input for various applications, including 3D
reconstruction, pose estimation, tracking, and structure-from-motion. Despite
its utility, the field of dense long-term tracking, especially over wide
baselines, has not been extensively explored. This paper extends the concept of
combining multiple optical flows over logarithmically spaced intervals as
proposed by MFT. We demonstrate the compatibility of MFT with different optical
flow networks, yielding results that surpass their individual performance.
Moreover, we present a simple yet effective combination of these networks
within the MFT framework. This approach proves to be competitive with more
sophisticated, non-causal methods in terms of position prediction accuracy,
highlighting the potential of MFT in enhancing long-term tracking applications.