Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives

Cross-view feature matching aims to establish reliable correspondences across images with large viewpoint variations. Over the past decade, the field has evolved from task-specific models toward increasingly unified and generalizable correspondence models, with recent progress further driven by the emergence of vision foundation models (VFMs). Despite these advances, existing studies remain highly diverse in their problem formulations, model architectures, training paradigms, and evaluation prot

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ethics.ai (11 August 2026), “Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives,” evidence record 18679, https://ethics.ai/record/18679 (originally published by arXiv fairness query).

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