Coarse to finE Registration

Coarse-to-fine registration is a crucial image processing technique aiming to accurately align two images or point clouds by iteratively refining an initial, approximate alignment. Current research emphasizes efficient and robust methods, employing architectures like multi-layer perceptrons (MLPs), transformers, and graph convolutional networks (GCNs), often within optimization-based or deep learning frameworks, to achieve accurate registration even with large deformations or noisy data. This technique is vital across diverse fields, including medical image analysis (e.g., brain tumor tracking) and robotics (e.g., surgical navigation), where precise alignment is essential for diagnosis, treatment planning, and automated processes. The ongoing focus is on improving accuracy, speed, and robustness across various data types and challenging scenarios.