Structure-Guided Capacity Allocation for Sparse-View Indoor Gaussian Splatting

Sparse-view indoor reconstruction with 3D Gaussian Splatting remains challenging because limited cross-view supervision leaves weakly textured surfaces and occluded structures poorly constrained. Unrestricted densification can further reinforce unreliable geometry by allocating additional capacity to ambiguous regions. We propose a structure-guided capacity allocation framework that uses explicit Gaussian roles to guide candidate selection and constrain split directions. Starting from scale-consistent multi-view geometry, we initialize ordinary, line, and planar Gaussians, with planar roles further distinguished by texture complexity. For candidate selection, we introduce a type-calibrated score that combines gradient-based fitting demand with rendering contributions normalized within each structural type. The resulting scores prioritize candidates under a shared Top-$K$ growth budget. For growth, persistent structural priors restrict line and high-texture planar split displacements to one-dimensional tangents and two-dimensional tangent subspaces, respectively, while low-texture planar Gaussians are excluded from densification. Descendants inherit these structural attributes, retaining the growth constraints across successive densification stages, while geometric regularization stabilizes subsequent optimization. Experiments demonstrate improved perceptual quality and better preservation of indoor structural details in novel-view renderings compared with representative baselines.