AlphaRefine: Opacity-Routed Refinement for Feed-Forward Driving Scene Reconstruction

Feed-forward driving scene reconstruction has emerged as a scalable alternative to per-scene optimization, but still struggles to produce globally consistent appearance and fine structural details. Recent efforts close this gap through diffusion-based post-hoc refinement, which applies a uniform and computationally heavy generative enhancement. However, this uniform treatment overlooks the actual error structure of feed-forward renderings. Through empirical analysis on multiple feed-forward models, we observe that rendering errors are not spatially homogeneous but strongly correlated with accumulated opacity, where low-opacity regions are dominated by low-frequency appearance shifts and high-opacity regions suffer primarily from high-frequency structural loss. Building on this observation, we identify a complementary correspondence between these two error regimes and two in-context correction signals encoded in the source view. Specifically, the source image serves as appearance context for low-frequency correction, while the source-view residual provides error context for high-frequency recovery. Based on this insight, we introduce **AlphaRefine**, a lightweight post-hoc refinement framework that routes the two corrections by accumulated opacity, applicable to any frozen feed-forward driving reconstruction model. Under the same evaluation protocols, our method achieves new state-of-the-art among feed-forward methods on both Waymo and nuScenes, boosting STORM to $28.32$ dB ($+2.51$ dB) and DrivingForward to $29.83$ dB ($+2.46$ dB), while running over $9\times$ faster than single-step diffusion refiners at only $6$ ms per image. Code will be released.