{
  "id": 18679,
  "url": "https://arxiv.org/abs/2608.11093v1",
  "title": "Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives",
  "summary": "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",
  "authors": "Songlin Du, Xiaoyong Lu, Zeyu Wu, Xiaobo Lu, Guobao Xiao, Bin Fan, Jiayi Ma, Takeshi Ikenaga",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T16:00:40.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18679",
  "original_url": "https://arxiv.org/abs/2608.11093v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}