ATR-UMMIM: A Benchmark Dataset for UAV-Based Multimodal Image Registration under Complex Imaging Conditions

TL;DR

ATR-UMMIM is the first comprehensive benchmark dataset for multimodal image registration in UAV-based aerial scenarios, facilitating the development of robust registration methods under diverse real-world conditions.

Contribution

This paper introduces ATR-UMMIM, a large-scale, multi-scenario dataset with high-quality annotations for multimodal registration in UAV applications, filling a critical gap in existing resources.

Findings

Provides 7,969 triplets of registered visible, infrared, and RGB images.

Includes diverse scenarios with varying altitude, angles, and weather conditions.

Enables benchmarking of registration robustness and downstream perception tasks.

Abstract

Multimodal fusion has become a key enabler for UAV-based object detection, as each modality provides complementary cues for robust feature extraction. However, due to significant differences in resolution, field of view, and sensing characteristics across modalities, accurate registration is a prerequisite before fusion. Despite its importance, there is currently no publicly available benchmark specifically designed for multimodal registration in UAV-based aerial scenarios, which severely limits the development and evaluation of advanced registration methods under real-world conditions. To bridge this gap, we present ATR-UMMIM, the first benchmark dataset specifically tailored for multimodal image registration in UAV-based applications. This dataset includes 7,969 triplets of raw visible, infrared, and precisely registered visible images captured covers diverse scenarios including flight altitudes from 80m to 300m, camera angles from 0{\deg} to 75{\deg}, and all-day, all-year temporal variations under rich weather and illumination conditions. To ensure high registration quality, we design a semi-automated annotation pipeline to introduce reliable pixel-level ground truth to each triplet. In addition, each triplet is annotated with six imaging condition attributes, enabling benchmarking of registration robustness under real-world deployment settings. To further support downstream tasks, we provide object-level annotations on all registered images, covering 11 object categories with 77,753 visible and 78,409 infrared bounding boxes. We believe ATR-UMMIM will serve as a foundational benchmark for advancing multimodal registration, fusion, and perception in real-world UAV scenarios. The datatset can be download from https://github.com/supercpy/ATR-UMMIM