[2512.02172] SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.02172 (cs) [Submitted on 1 Dec 2025 (v1), last revised 28 Mar 2026 (this version, v2)] Title:SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting Authors:Pranav Asthana, Alex Hanson, Allen Tu, Tom Goldstein, Matthias Zwicker, Amitabh Varshney View a PDF of the paper titled SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting, by Pranav Asthana and 5 other authors View PDF HTML (experimental) Abstract:3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis, motivating interest in generating higher-resolution renders than those available during training. A natural strategy is to apply super-resolution (SR) to low-resolution (LR) input views, but independently enhancing each image introduces multi-view inconsistencies, leading to blurry renders. Prior methods attempt to mitigate these inconsistencies through learned neural components, temporally consistent video priors, or joint optimization on LR and SR views, but all uniformly apply SR across every image. In contrast, our key insight is that close-up LR views may contain high-frequency information for regions also captured in more distant views and that we can use the camera pose relative to scene geometry to inform where to add SR content. Building on this insight, we propose SplatSuRe, a method that selectively applies SR content only in undersampled regions lacking high-fr...