Planar-Guided Gaussian Splatting with Texture-Complexity-Based Initialization

Indoor scene reconstruction remains challenging due to the prevalence of low-texture regions such as walls, floors, and ceilings, where weak photometric signals hinder accurate geometric recovery. While 3D Gaussian Splatting (3DGS) achieves impressive novel view synthesis, existing methods struggle with geometric accuracy in textureless areas due to uniform treatment of scene regions. We propose a texture-complexity-based 3D Gaussian Splatting strategy that leverages geometric priors for high-fidelity indoor reconstruction. Our method extracts planar priors through Manhattan frame alignment and refines them with Segment Anything Model (SAM) masks, enabling texture-aware initialization: planar priors guide Gaussian placement in low-texture regions, while dense feature matching ensures accurate initialization in high-detail areas. During optimization, geometric regularization through depth-plane loss, normal-surface loss, and normal-consistency loss maintains structural integrity. Evaluations on ScanNet++, MuSHRoom, and Replica datasets demonstrate state-of-the-art performance, with training completed in under 1 h. Our approach balances geometric accuracy with photometric fidelity, providing a practical solution for high-fidelity indoor mesh extraction from Gaussian representations.