MAGS: Mask Augmented Gaussian Splatting for reflective scenes

Novel view synthesis of reflective scenes remains challenging because different combinations of geometry, materials, and illumination can explain similar observed appearances. This ambiguity can lead to inconsistent material estimates within regions that share a common material, potentially degrading reflection rendering at novel viewpoints. We introduce Mask-Augmented Gaussian Splatting (MAGS), a framework that improves reflective novel view synthesis through mask-guided material regularization. Building on a physically based 2D Gaussian Splatting framework, MAGS uses segmentation masks as regional priors and penalizes deviations of rendered material attributes from their mask-level means. The resulting gradients propagate through differentiable rasterization to the underlying Gaussian material parameters, encouraging regional material consistency while allowing rendered appearance to vary across viewpoints. Experiments demonstrate competitive performance on the real-world Ref-Real benchmark and the best average PSNR, SSIM, and LPIPS among the compared methods on the synthetic ShinyBlender benchmark.