StratGS: Layered Surface Recovery for Transparent Objects from Gaussian Splatting

Reconstructing transparent and semi-transparent scenes requires recovering multiple surfaces along a viewing ray. In nested scenes, dominant shell contributions can suppress the learning of interior structures. Reducing their contributions to a single depth can discard weak surface evidence or place geometry between distinct interfaces. We present StratGS, a Gaussian Splatting framework that combines nested-scene optimization with multiple-surface mesh reconstruction. After learning the whole scene, our nested refinement fixes the shell and background Gaussians and caps the shell's rendering contribution while optimizing the interior representation. Range-Aware Depth Extraction (RADE) retains local depth estimates from multiple search ranges for independent multi-view reconstruction. Independent TSDF integration produces fine meshes for all queries and a coarse candidate from the widest query using a broader truncation distance. To control the addition of coarse geometry, Fusion Using Scale-aware Exclusion (FUSE) preserves all fine triangles as a fixed coverage reference and clips the coarse mesh to retain fragments beyond that region. To evaluate coverage of transparent enclosures and the objects inside them, we introduce TransNest, eight synthetic multi-view scenes with separate ground-truth geometry for both regions. On existing benchmarks, the resulting meshes achieve mean Chamfer distances of 0.52 on αSurf and 1.553 mm on TransLab, and a mean one-way CD (s→d) of 3.276 mm on ClearPose. These are the lowest mean errors among the compared methods. On TransNest, StratGS achieves the lowest CD on all eight scenes and the lowest mean ground-truth-to-mesh distances for both enclosures and interiors.