RIFT2: Speeding-up RIFT with A New Rotation-Invariance Technique
This work addresses a practical bottleneck for researchers and practitioners in computer vision by making multimodal feature matching more efficient, though it is incremental as it builds directly on the existing RIFT method.
The paper tackles the high computational cost of the RIFT method for multimodal image matching by proposing RIFT2, which uses a new rotation invariance technique based on dominant index value, reducing running time and memory consumption by almost 3 times while maintaining similar matching performance.
Multimodal image matching is an important prerequisite for multisource image information fusion. Compared with the traditional matching problem, multimodal feature matching is more challenging due to the severe nonlinear radiation distortion (NRD). Radiation-variation insensitive feature transform (RIFT)~\cite{li2019rift} has shown very good robustness to NRD and become a baseline method in multimodal feature matching. However, the high computational cost for rotation invariance largely limits its usage in practice. In this paper, we propose an improved RIFT method, called RIFT2. We develop a new rotation invariance technique based on dominant index value, which avoids the construction process of convolution sequence ring. Hence, it can speed up the running time and reduce the memory consumption of the original RIFT by almost 3 times in theory. Extensive experiments show that RIFT2 achieves similar matching performance to RIFT while being much faster and having less memory consumption. The source code will be made publicly available in \url{https://github.com/LJY-RS/RIFT2-multimodal-matching-rotation}