GaussAsset: Evidence-Grounded Segmentation into Au- ditable Object Assets in Indoor 3D Gaussian Scenes

3D Gaussian Splatting provides an explicit rendering representation for indoor scenes, but turning that representation into objects remains difficult: real captures contain floaters and missing coverage, and exhibit view-dependent effects such as transparency and reflections. Methods built for clean point clouds or surface annotations do not account for these Gaussian-specific artifacts and do not decide which Gaussians belong to which object, while fixed-view lifting methods do not establish persistent ownership. To address this problem, we present GaussAsset, an evidence-grounded met hod that turns a Gaussian carrier into auditable object assets by separating semantic proposal from ownership commitment. A frozen vision- language model proposes scene concepts and flags uncertain relations; a geometry- constrained executor lifts SAM3 masks through rendered depth, associates them across views, and commits mutually exclusive ownership over source Gaussians only when the evidence supports it, recording every decision in an auditable ledger. To evaluate this setting, we introduce GaussAsset-100, a benchmark of 100 furnished scenes with 3,590 human-verified object cuboids. The annotations target countable, exportable entities above a size gate and are provided as object- level cuboids, the unit downstream users export; membership diagnostics are box-derived and explicitly marked. Under a controlled shared-observation protocol that fixes the inputs for all methods, GaussAsset achieves the strongest object recall and all-GT bbox mIoU among the compared methods (77.5/67.2 at IoU 0.25/0.50; 55.4 bbox mIoU) and 56.1 scene-macro semantic mIoU without target- domain training. Together, these results support separating semantic proposal from ownership commitment as a practical route from a Gaussian carrier to auditable, exportable object assets.