Uncertainty- and Parallax-Aware Gaussian Splatting for Indoor Object-Centric Reflective Scenes

Specular reflections remain a major challenge for novel view synthesis, requiring both reliable localization of reflective regions and accurate modeling of their view-dependent appearance. However, reflective regions are difficult to localize consistently across views, while existing reflection models often assume distant environment lighting. This assumption breaks down indoors, where finite-distance scene geometry induces strong reflection parallax, leading to baked-in appearance and view-inconsistent artifacts. To address these challenges, we leverage learned photometric uncertainty to derive metallic supervision through Uncertainty-Guided Metallic Supervision (UGMS), enabling stable diffuse-specular decomposition, and incorporate Parallax-Corrected Cubemaps (PCC) to account for finite-distance environment lighting. We further introduce IndoorRef12, a synthetic benchmark featuring near-field reflection parallax and cluttered backgrounds. It comprises 12 diverse indoor environments with varying scene layouts, reflective-object configurations, and background complexity. Extensive experiments demonstrate substantial improvements in reflection reconstruction, particularly in challenging indoor scenes with strong near-field parallax. We will release our code and IndoorRef12 dataset.