UniCorrn: Unified Correspondence Transformer Across 2D and 3D
Visual correspondence across image-to-image (2D-2D), image-to-point cloud (2D-3D), and point cloud-to-point cloud (3D-3D) geometric matching forms the foundation for numerous 3D vision tasks. Despite sharing a similar problem structure, current methods use task-specific designs with separate models for each modality combination. We present UniCorrn, the first correspondence model with shared weights that unifies geometric matching across all three tasks. Our key insight is that Transformer attention naturally captures cross-modal feature similarity. We propose a dual-stream decoder that maintains separate appearance and positional feature streams. This design enables end-to-end learning t...
| Authors | Prajnan Goswami, Tianye Ding, Feng Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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