GlassGS: Geometry and Concept-Aware 3D Gaussian Splatting for Reflective Enclosures

Reconstructing 3D scenes of objects displayed in glass enclo-sures remains a significant challenge for 3D Gaussian Splatting (3DGS),particularly in museum and exhibition hall settings where transparentprotective cases are ubiquitous. The high specular reflections (HSR) gen-erated by glass surfaces severely degrade reconstruction quality, yet theirunderlying properties and impact on 3DGS remain poorly understood.Through systematic investigation, we reveal two insights into HSR be-havior in 3DGS: (1) Geometry-inconsistency property: HSR causes 3Dpoints to exhibit drastically different appearances across viewpoints, fun-damentally violating the geometric consistency assumption that under-pins 3DGS; (2) Concept-preservation property: Despite corrupting lo-cal appearance, HSR has remarkably limited influence when learninghigh-level object concepts, as the true object structure remains intactbeneath the reflective layer. Motivated by these insights, we proposeGlassGS, a geometry and concept-aware 3DGS framework with an it-erative two-stage optimization. First, we compute geometry-consistencymaps for each input view to evaluate the appearance variations acrossviewpoints, explicitly identifying HSR-affected pixels by measuring theappearance variations of 3D points across views. Second, we introducea geometry-inconsistency-guided object concept learning module thatleverages these maps to learn robust object representations while activelysuppressing reflection interference. The two stages mutually reinforceeach other through iterative refinement, improving both inconsistencydetection and concept learning. For this new task, we collect the firstdataset specifically designed for 3DGS reconstruction of glass-enclosedobjects. Extensive experiments demonstrate that GlassGS significantlyoutperforms state-of-the-art methods, enabling high-fidelity digitizationof glass-enclosed scenes and opening new possibilities for virtual museumexperiences.