CAM3R: Camera-Agnostic Model for 3D Reconstruction

Recovering dense 3D geometry from unposed images remainsa foundational challenge in computer vision. Current state-of-the-artmodels are predominantly trained on perspective datasets, which implic-itly constrains them to a standard pinhole camera geometry. As a result,these models suffer from significant geometric degradation when appliedto wide-angle imagery captured via non-rectilinear optics, such as fisheyeor panoramic sensors. To address this, we present CAM3R, a Camera-Agnostic, feed-forward Model for 3D Reconstruction capable of process-ing images from wide-angle camera models without prior calibration.Our framework consists of a two-view network which is bifurcated into aRay Module (RM) to estimate per-pixel ray directions and a Cross-viewModule (CVM) to infer radial distance with confidence maps, pointmaps,and relative poses. To unify these pairwise predictions into a consistent3D scene, we introduce a Ray-Aware Global Alignment framework forpose refinement and scale optimization while strictly preserving the pre-dicted local geometry. Extensive experiments on various camera modeldatasets, including panorama, fisheye and pinhole imagery, demonstratethat CAM3R establishes a new state-of-the-art in pose estimation andreconstruction.