APPEARANCE-INDUCED SURFACE BIAS AND SCORE SEPARATION IN GAUSSIAN SPLATTING

Gaussian splatting reconstructs surfaces by fitting geometry and a finite appearance model jointly to multi view images, a coupling that the prevailing account interprets as an ambiguity in which several shapes explain the images equally well. We show that finite appearance induces a strict preference for physically worse surfaces, a phenomenon we term appearance induced surface bias. On seven DTU scenes, allowing the geometry of 2D Gaussian splatting to move raises test PSNR by 2.9 dB while the reconstructed surface degrades in every scene. We propose Score Separation, a geometry inference framework that retains a regularized estimator for appearance and selects geometry with an independent score. Two mechanisms underlie the bias. Near the true surface, an appearance residual aligned with the image change of a surface displacement shifts the preferred surface, and the penalty that regularizes appearance also enters the ranking of candidate surfaces. Variable projection characterizes the first mechanism, and primal and dual certificates establish strict preferences for worse surfaces in 19 of 23 reconstructed scenes independently of the optimizer. Score Separation reduces controlled recovery error from 6.09 to 4.75 mm, lowers error on unseen materials by 13.7% through its complexity corrected score, and improves reconstructed surfaces where the penalty dominates the descent. As a secondary diagnostic, the contribution depth error reveals surface deterioration that image quality and mean depth conceal