# Radiation, Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching

arXiv: https://arxiv.org/abs/2609.06343 · HTML: https://mlsanity.com/paper/2609.06343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 51 references that are in the map.
- First posted on arXiv 6 Sep 2026.

## Builds on (most cited first, 22 of 22)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning](https://mlsanity.com/paper/2003.02437/) (2020)
- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed](https://mlsanity.com/paper/2403.04765/) (2024)
- [Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters](https://mlsanity.com/paper/1904.00889/) (2019)
- [XoFTR: Cross-modal Feature Matching Transformer](https://mlsanity.com/paper/2404.09692/) (2024)
- [MINIMA: Modality Invariant Image Matching](https://mlsanity.com/paper/2412.19412/) (2024)
- [MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training](https://mlsanity.com/paper/2501.07556/) (2025)
- [Beyond Cartesian Representations for Local Descriptors](https://mlsanity.com/paper/1908.05547/) (2019)
- [Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity](https://mlsanity.com/paper/2103.16871/) (2021)
- [JamMa: Ultra-lightweight Local Feature Matching with Joint Mamba](https://mlsanity.com/paper/2503.03437/) (2025)
- [R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor](https://mlsanity.com/paper/2212.02277/) (2022)
- [SceneGlue: Scene-Aware Transformer for Feature Matching without Scene-Level Annotation](https://mlsanity.com/paper/2604.13941/) (2026)

## Abstract (arXiv)

Multimodal image matching is a fundamental task for multi-source information fusion. However, geometric distortions and nonlinear radiometric differences (NRD) severely limit performance, especially under radiometric, rotation, and scale variations. To address this issue, we propose a radiation, rotation, and scale invariant (RRSI) feature descriptor. First, a dual-head regional sampling (DHRS) module simultaneously performs Cartesian and Log-Polar sampling on keypoint neighborhoods, retaining spatial structural properties while enhancing robustness to rotation and scale variations. We then jointly encode geometric and radiometric relations between multimodal images in a unified deep feature space, enabling feature encoding, interaction, and fusion across intra-modal, dual-head sampled, and inter-modal regions. Furthermore, we introduce a bidirectional cross-modal generative reconstruction constraint during training. By decoding implicit features into structural patches of the counterpart modality, this mechanism anchors modality-invariant geometric topologies without additional inference overhead. Experiments on optical-infrared and optical-SAR datasets demonstrate highly competitive matching performance and strong robustness to rotation and scale variations. RRSI supports the full rotation range from 0 to 360 degrees and scale factors up to four. Its generalization ability is further validated on multimodal images from computer vision, remote sensing, and medical imaging. The implementation will be made publicly available at https://github.com/yeyuanxin110/RRSI .