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 through stack-able layers while supporting flexible query-based correspondence estimation across heterogeneous modalities. Our architecture employs modality-specific backbones followed by shared encoder and decoder components, trained jointly on diverse data combining pseudo point clouds from depth maps with real 3D correspondence annotations. UniCorrn achieves competitive performance on 2D-2D matching and surpasses prior state-of-the-art by 8% on 7Scenes (2D-3D) and 10% on 3DLoMatch (3D-3D) in registration recall. Project website: https://neu-vi.github.io/UniCorrn

翻译：图像到图像（2D-2D）、图像到点云（2D-3D）以及点云到点云（3D-3D）几何匹配中的视觉对应构成了众多3D视觉任务的基础。尽管问题结构相似，但当前方法采用任务特定的设计，为每种模态组合使用独立的模型。我们提出UniCorrn，这是首个具有共享权重的对应模型，统一了所有三种任务的几何匹配。我们的关键洞察在于Transformer注意力机制能够自然地捕捉跨模态特征相似性。我们提出一个双流解码器，保持独立的外观特征流和位置特征流。该设计支持通过可堆叠层进行端到端学习，同时支持跨异构模态的灵活基于查询的对应估计。我们的架构采用模态特定的主干网络，随后是共享的编码器和解码器组件，在结合深度图生成的伪点云与真实3D对应标注的多样化数据上进行联合训练。UniCorrn在2D-2D匹配上取得了具有竞争力的性能，并在注册召回率上相较先前最优方法在7Scenes（2D-3D）上提升了8%，在3DLoMatch（3D-3D）上提升了10%。项目网站：https://neu-vi.github.io/UniCorrn