Modality-Aware Feature Matching: A Comprehensive Review of Single- and Cross-Modality Techniques

• URL: http://arxiv.org/abs/2507.22791v1
• Date: Wed, 30 Jul 2025 15:56:36 GMT
• Title: Modality-Aware Feature Matching: A Comprehensive Review of Single- and Cross-Modality Techniques
• Authors: Weide Liu, Wei Zhou, Jun Liu, Ping Hu, Jun Cheng, Jungong Han, Weisi Lin,
• Abstract summary: Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM.<n>This survey comprehensively reviews modality-based feature matching, exploring traditional handcrafted methods and contemporary deep learning approaches.
• Score: 91.26187560114381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM. This survey comprehensively reviews modality-based feature matching, exploring traditional handcrafted methods and emphasizing contemporary deep learning approaches across various modalities, including RGB images, depth images, 3D point clouds, LiDAR scans, medical images, and vision-language interactions. Traditional methods, leveraging detectors like Harris corners and descriptors such as SIFT and ORB, demonstrate robustness under moderate intra-modality variations but struggle with significant modality gaps. Contemporary deep learning-based methods, exemplified by detector-free strategies like CNN-based SuperPoint and transformer-based LoFTR, substantially improve robustness and adaptability across modalities. We highlight modality-aware advancements, such as geometric and depth-specific descriptors for depth images, sparse and dense learning methods for 3D point clouds, attention-enhanced neural networks for LiDAR scans, and specialized solutions like the MIND descriptor for complex medical image matching. Cross-modal applications, particularly in medical image registration and vision-language tasks, underscore the evolution of feature matching to handle increasingly diverse data interactions.

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