3 papers
cs.LG2026
Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives
Songlin Du, Xiaoyong Lu, Zeyu Wu +5
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…
cs.CV2026
SceneGlue: Scene-Aware Transformer for Feature Matching without Scene-Level Annotation
Songlin Du, Xiaoyong Lu, Yaping Yan +3
Local feature matching plays a critical role in understanding the correspondence between cross-view images. However, traditional methods are constrained by the inherent local natur…
cs.CV2025
JamMa: Ultra-lightweight Local Feature Matching with Joint Mamba
Xiaoyong Lu, Songlin Du
Existing state-of-the-art feature matchers capture long-range dependencies with Transformers but are hindered by high spatial complexity, leading to demanding training and highlate…