Bifröst: Multi-Sensor Diffusion Model for Cross-View Panoramic Synthesis

Synthesizing ground-level 360° panoramas from overhead imagery typically assumes access to sub-meter aerial photography (0.3m/px). In practice, this restricts synthesis to densely surveyed metropolitan regions. Moving to globally available 10m/px Sentinel-2 multi-spectral data avoids this coverage bottleneck, but the low-resolution conditioning yields severe structural ambiguity. To overcome this, we introduce Bifröst, a diffusion framework designed to run inference strictly on open-access satellite data (10m/px Sentinel-2 and AlphaEarth embeddings) while retaining high-resolution geometric detail. Rather than feeding high-resolution imagery into the generator, we use it solely as training-time supervision via a frozen Perspective-Inversion Critic (PIC) that enforces structural consistency. This gives us the novel ability to generate street-view using only highly available low-resolution information. To handle cross-view spatial transformation, we align overhead polar coordinates directly with equirectangular spherical grids inside cross-attention layers. On a benchmark of 560k spatially disjoint locations, Bifröst eliminates the blur typical of low-resolution baselines, cutting relative depth error (AbsRel) from 0.138 to 0.106, matching models conditioned on true sub-meter aerial inputs (AbsRel 0.101) while improving FID by 77.86% over prior methods. On the public CVUSA dataset, Bifröst achieves an improvement of 68.4% FID over prior methods. These results demonstrate that the proposed approach effectively overcomes the need for high resolution information for cross-view generation.