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Multimodal image matching is an important prerequisite for multisource imageinformation fusion. Compared with the traditional matching problem, multimodalfeature matching is more challenging due to the severe nonlinear radiationdistortion (NRD). Radiation-variation insensitive feature transform(RIFT)~\cite{li2019rift} has shown very good robustness to NRD and become abaseline method in multimodal feature matching. However, the high computationalcost for rotation invariance largely limits its usage in practice. In thispaper, we propose an improved RIFT method, called RIFT2. We develop a newrotation invariance technique based on dominant index value, which avoids theconstruction process of convolution sequence ring. Hence, it can speed up therunning time and reduce the memory consumption of the original RIFT by almost 3times in theory. Extensive experiments show that RIFT2 achieves similarmatching performance to RIFT while being much faster and having less memoryconsumption. The source code will be made publicly available in\url{https://github.com/LJY-RS/RIFT2-multimodal-matching-rotation}

RIFT2: Speeding-up RIFT with A New Rotation-Invariance Technique