IndoorSplat: Enhanced Indoor Scene Reconstruction with Structured 2D Gaussian Splatting

Geometric reconstruction of indoor scenes poses unique challenges due to their inherent characteristics, such as large textureless surfaces, complex occlusions, and intricate geometric structures. While recent 2D/3D Gaussian Splatting-based methods have achieved impressive performance in general scene reconstruction, their direct application to indoor settings often leads to noisy, incomplete, or geometrically inconsistent results. In this paper, we propose a novel framework IndoorSplat to bridge this gap and enable high-quality indoor scene geometry reconstruction based on 2D Gaussian Splatting (2DGS). To this end, we first revisit the importance of Gaussian initialization in the indoor scene reconstruction process and introduce a carefully designed dense initialization strategy. This strategy leverages local geometric priors to densify point clouds estimated by an advanced feed-forward model and utilizes them to initialize the structure of Gaussian primitives. In addition, to better capture the rich structural details in indoor scenes, we introduce an iterative adaptive densification strategy that accelerates convergence in complex regions by applying more aggressive splitting and cloning policies to Gaussians with consistent activation. Extensive experiments demonstrate that our method achieves state-of-the-art performance by producing more geometrically accurate and complete surface reconstructions compared to existing approaches.