{"ID":2887288,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01697","arxiv_id":"2508.01697","title":"Register Anything: Estimating \"Corresponding Prompts\" for Segment Anything Model","abstract":"Establishing pixel/voxel-level or region-level correspondences is the core challenge in image registration. The latter, also known as region-based correspondence representation, leverages paired regions of interest (ROIs) to enable regional matching while preserving fine-grained capability at pixel/voxel level. Traditionally, this representation is implemented via two steps: segmenting ROIs in each image then matching them between the two images. In this paper, we simplify this into one step by directly \"searching for corresponding prompts\", using extensively pre-trained segmentation models (e.g., SAM) for a training-free registration approach, PromptReg. Firstly, we introduce the \"corresponding prompt problem\", which aims to identify a corresponding Prompt Y in Image Y for any given visual Prompt X in Image X, such that the two respectively prompt-conditioned segmentations are a pair of corresponding ROIs from the two images. Secondly, we present an \"inverse prompt\" solution that generates primary and optionally auxiliary prompts, inverting Prompt X into the prompt space of Image Y. Thirdly, we propose a novel registration algorithm that identifies multiple paired corresponding ROIs by marginalizing the inverted Prompt X across both prompt and spatial dimensions. Comprehensive experiments are conducted on five applications of registering 3D prostate MR, 3D abdomen MR, 3D lung CT, 2D histopathology and, as a non-medical example, 2D aerial images. Based on metrics including Dice and target registration errors on anatomical structures, the proposed registration outperforms both intensity-based iterative algorithms and learning-based DDF-predicting networks, even yielding competitive performance with weakly-supervised approaches that require fully-segmented training data.","short_abstract":"Establishing pixel/voxel-level or region-level correspondences is the core challenge in image registration. The latter, also known as region-based correspondence representation, leverages paired regions of interest (ROIs) to enable regional matching while preserving fine-grained capability at pixel/voxel level. Traditi...","url_abs":"https://arxiv.org/abs/2508.01697","url_pdf":"https://arxiv.org/pdf/2508.01697v1","authors":"[\"Shiqi Huang\",\"Tingfa Xu\",\"Wen Yan\",\"Dean Barratt\",\"Yipeng Hu\"]","published":"2025-08-03T10:00:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}