[2603.23297] Drop-In Perceptual Optimization for 3D Gaussian Splatting

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.23297 (cs) [Submitted on 23 Mar 2026] Title:Drop-In Perceptual Optimization for 3D Gaussian Splatting Authors:Ezgi Ozyilkan, Zhiqi Chen, Oren Rippel, Jona Ballé, Kedar Tatwawadi View a PDF of the paper titled Drop-In Perceptual Optimization for 3D Gaussian Splatting, by Ezgi Ozyilkan and 4 other authors View PDF HTML (experimental) Abstract:Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a diverse set of distortion losses. We conduct the first-of-its-kind large-scale human subjective study on 3DGS, involving 39,320 pairwise ratings across several datasets and 3DGS frameworks. A regularized version of Wasserstein Distortion, which we call WD-R, emerges as the clear winner, excelling at recovering fine textures without incurring a higher splat count. WD-R is preferred by raters more than $2.3\times$ over the original 3DGS loss, and $1.5\times$ over current best method Perceptual-GS. WD-R also consistently achieves state-of-the-art LPIPS, DISTS, and FID scores across various datasets, and generalizes across recent frameworks, such as Mip-Splatting and Scaffold-GS, where replacing the original loss with WD-R consistently enhances perceptual quality within a similar...