NativeRefine: Nuisance-Aware Refinement of Feed-Forward 3D Geometry

We introduce NativeRefine, a portable, training-free refinement layer that turns frozen feed-forward 3D predictions into scene-adapted geometry using RGB correspondences. Its key principle is to separate fitting freedom from output complexity. Bounded, centered source-ray variables provide temporary structural flexibility for camera fitting; a second stage fixes the fitted cameras and refits a compact, native-relative depth field. The emitted reconstruction retains native detail and preserves the window-wide geometric mean of depth exactly. Across four frozen backbones and 600 windows from three benchmarks, 2,400 model–window evaluations demonstrate broad applicability without retraining or architecture-specific tuning. With known intrinsics, point and shared-scale translation errors improve together in 10 of 12 cohort–backbone cells against each of native prediction and bundle adjustment with the same dense refit. Two additional recordings tested after method lock retain translation gains of 27.6% and 26.9% over native MapAnything and Pi3X. RGB-only transfer to VGGT and DA3 improves point error in 9 of 12 cells without external calibration. The complete 1,200-evaluation extension runs on a 16 GB consumer GPU, bringing compact, native-preserving optimization to existing reconstruction models.