PanoDPG: Decoupled Pose and Geometry Estimation for High-Resolution Panoramic 3D Reconstruction

Feed-forward multi-view reconstruction has achieved remarkable progress in jointly recovering camera poses and dense geometry, yet scaling it to high-resolution panoramas remains prohibitively expensive. At the core of this inefficiency is a task-agnostic design that applies the same high-resolution representation and global interaction to both pose estimation and geometry reconstruction, despite their distinct computational and representational requirements. Pose estimation relies primarily on global context and is less sensitive to spatial resolution, whereas dense geometry reconstruction requires fine-grained visual details and is strongly affected by panoramic projection distortion. Based on this observation, we propose PanoDPG, a dual-branch framework that decouples pose estimation from dense geometry reconstruction in both resolution and projection representation. PanoDPG performs global pose reasoning on low-resolution equirectangular projections (ERP), while reconstructing detailed geometry from high-resolution cubemaps with reduced spherical distortion. To preserve the geometric dependencies between the two branches, we introduce a register-token-based interaction mechanism that efficiently transfers global information, aligns the scales of estimated poses and reconstructed geometry, and promotes multi-view consistency without full global attention over high-resolution features. Experiments on multi-datasets show that PanoDPG achieves reconstruction accuracy while reducing computational cost, demonstrating an effective and scalable solution for high-resolution panoramic reconstruction.