FFBA: Feed-Forward Bundle Adjustment

We propose feed-forward bundle adjustment (FFBA), which recasts per-scene iterative BA as a single forward pass of a learned refinement model with update rules shared across scenes. Given initial cameras and geometry from a geometric foundation model (GFM) and fixed multi-view tracks, FFBA constructs a sparse heterogeneous graph of cameras, 3D points and 2D observations. It then refines poses, focal lengths and 3D points through a geometry-conditioned graph network stacked with geometric graph aggregation layers. These layers update observation, camera and point features via graph message propagation, and filter unreliable observations through uncertainty-guided confidence estimation. A confidence-weighted reprojection loss trains the whole framework end-to-end using only in-house data, without manual annotation. To support training across datasets and GFMs, we develop a standardized sample-construction pipeline that yields over 0.9M samples from seven datasets and three GFMs with shared multi-view tracks. Experiments on three in-domain and four cross-domain datasets show that FFBA consistently improves reprojection consistency and camera accuracy over GFM initializations. It also remains competitive with classical BA and EPO under domain shift, and reduces optimization cost by more than an order of magnitude, translating into a clear end-to-end efficiency advantage that is expected to grow with scene scale. Code and dataset will be shared upon paper acceptance.