Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction

Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) hasrecently emerged as a powerful paradigm for fast scene reconstruction.However, its performance degrades significantly in long image sequencesdue to cumulative camera pose estimation drift, which propagates er-rors into geometric modeling and severely limits rendering fidelity. Inthis work, we revisit the long-sequence bottleneck and identify posedrift as the primary factor restricting reconstruction quality. Further-more, while SfM-based pseudo ground-truth poses introduce sensor noise,purely rendering-based supervision often leads to optimization instabil-ity and local minima due to the entangled optimization of geometryand pose. To address the challenges, we propose a synergistic pose-free framework that explicitly couples geometry and appearance via aRaymap-Guided Coupling Module (RGC). Concretely, we anchor Gaus-sian centers to raymap-induced geometry and jointly optimize RGB re-construction, raymap consistency, and camera regularization under a uni-fied objective, yielding a bidirectional feedback loop: stronger geometryimproves rendering, and appearance supervision in turn refines geometryand pose. To further stabilize learning across wide temporal ranges, weintroduce a Dual-Frequency Viewpoint Scheduling strategy that com-bines easy-to-hard interval expansion with replay of short-interval pairs.Extensive experiments across in-domain and cross-domain datasets showconsistent gains in both rendering and pose estimation, with notably im-proved robustness on long sequences. Ablation studies validate our cen-tral insight: explicitly designed geometry-appearance synergy is the keyto scalable and drift-robust pose-free feed-forward 3D reconstruction.