BridgeView: Enhancing Cross-View Consistency of Vision Foundation Models via Frozen Opposite-View Targets

Vision foundation models provide powerful frozen features, yet image-based pretraining does not require two observations of the same surface to share a descriptor. Existing approaches enforce this relation with an intermediate scene representation and a rendered teacher. Reconstruction is costly, and the rendered teacher is a visibility-weighted mixture in the reconstructed field, so matching it moves student features away from pretrained coordinates. Leaving that coordinate system raises scene-level agreement while frozen semantic transfer falls. Instead, we establish cross-view consistency directly inside the pretrained coordinate system. To this end, we propose BridgeView, which uses the frozen opposite-view descriptor as the teacher on a same-surface pair formed by calibrated geometry. When the encoder cell at that pixel mixes neighboring instances, a bounded residual refines this readout with instance context while remaining inside a cone of the frozen vector. When many pairs observe one instance relation, their backward updates are coordinated inside that relation. We validate BridgeView on indoor RGB-D benchmarks. Across DINOv3 backbones, it raises cross-view correspondence, semantic segmentation, visual localization, and TSDF map rendering, and trains over $10\times$ faster than SnD. Scaling the pair corpus to 3M further improves correspondence. These results suggest a scalable route for large vision foundation models to obtain cross-view consistency inside the pretrained coordinate system. Code is provided in the supplementary material.