PIH: Physical-intrinsic Harmonizer for Editable Neural Reconstruction

Editable neural driving scenes are essential for scalable simulation, yet neural reconstructions often produce severe artifacts under novel viewpoints and virtual-asset insertion. Existing harmonizers operating in RGB space learn to correct these heterogeneous failure patterns, making extension to new editing operations data-intensive. We reformulate the task as physical-intrinsic harmonization, using imperfect depth, normals, albedo, roughness, and illumination derived from the reconstructed scene as a compositional interface. This interface enables test-time edits by composing physical factors, eliminating the need for insertion-specific paired RGB correction data. To make this formulation practical for online simulation, we propose PIH, a one-step harmonizer trained directly with latent flow matching on the predicted velocity and perceptual supervision on its decoded clean endpoint. Mixed RGB-grounded and self-rollout histories provide temporal context during training to ensure temporal consistency and mitigate exposure bias. Human preference experiments across Waymo, nuPlan, and PandaSet show that PIH is preferred to the prior method in 77.41% of comparisons while using fewer paired training frames. PIH additionally enables illumination control.