Dense Matchers for Dense Tracking
This work addresses dense tracking for applications like 3D reconstruction and pose estimation, but it is incremental as it builds on existing MFT methods.
The paper tackled the problem of dense long-term tracking over wide baselines by extending the MFT framework to combine multiple optical flow networks, resulting in improved performance that surpasses individual networks and is competitive with non-causal methods in position prediction accuracy.
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.