Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion

Camera pose estimation is a key step in 3D reconstructionand view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based on learned view-graph aggregation. Our methodemploys a permutation-equivariant, edge-conditioned graph neural net-work that takes noisy pairwise relative poses as input and outputs glob-ally consistent camera extrinsics. The network is trained without ground-truth supervision, relying solely on a relative-pose consistency objective.This is followed by 3D point triangulation and robust bundle adjust-ment. Our approach is efficient, scalable to more than a thousand images,and robust to graph density. We evaluate our method on MegaDepth,1DSfM, Strecha, and BlendedMVS. These experiments demonstrate thatour method achieves superior rotation and translation accuracy com-pared to deep track-centric methods while registering more images acrossmany scenes, and competitive results compared to state-of-the-art clas-sical pipelines, while being much faster.