Image matching from handcrafted to deep features: a survey. (English) Zbl 1483.68439

Ma, Jiayi; Jiang, Xingyu; Fan, Aoxiang; Jiang, Junjun; Yan, Junchi

Int. J. Comput. Vis. 129, No. 1, 23-79 (2021).

Summary: As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

MSC:

68T45	Machine vision and scene understanding
68T07	Artificial neural networks and deep learning
68U10	Computing methodologies for image processing

Keywords:

image matching; graph matching; feature matching; registration; handcrafted features; deep learning

Software:

Theia; ASIFT; R2D2; L2-Net; BinGAN; PCA-SIFT; FREAK; BRISK; SOSNet; MonoSLAM; ORB-SLAM; YFCC100M; FusionGAN; SuperGlue; ASLFeat; ContextDesc; GMS; D2-Net; USAC; SuperPoint; LF-Net; ORB-SLAM2; PPFNet; 3DMatch; KAZE Features; SURF; Quicksilver; Go-ICP; LMNN; MAGSAC++; CycleGAN; SIFER; pix2pix; ApolloScape; SIFT; PointNet; HPatches; MatchNet; SIRF; Key.Net; FlowNet; TILDE; PASCAL VOC

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