NopeRoomGS: Pose-Free Indoor 3D Gaussian Splatting via Local-to-Global Optimization
Wenbo Li, Yan Xu, Mingde Yao, Fengjie Liang, Jiankai Sun, Menglu Wang, Guofeng Zhang, Linjiang Huang, Hongsheng Li
Novel view synthesis has been significantly advanced by the recent techniques, \eg, 3D Gaussian Splatting (3DGS). However, most current methods still require accurate camera pose estimation by Structure from Motion (SfM), otherwise, the performance would degrade significantly, especially in textureless indoor scenes. In this paper, we propose an efficient, pose-free 3DGS framework that can jointly optimize the 3D representation and camera poses. This framework enables robust reconstruction in textureless indoor regions where SfM generally fails. To enhance robustness in indoor scenes, where textureless regions and abrupt camera motions prevail, we introduce a local-to-global optimization scheme that progressively builds a global 3D representation from local estimations. Specifically, in the local stage, we construct a local neural scene representation supervised by depth foundation models to estimate camera pose and depth in textureless environments. In the global stage, the estimated local geometries are progressively merged into the global scene representation, and the camera poses are further refined. To address the abrupt camera motions in indoor scenes, we propose to decompose the rotation and translation components during pose optimization based on the local plane assumption and constrain the cross-frame consistency. Extensively benchmarked on public datasets, our method demonstrates state-of-the-art performance in both camera pose estimation and 3D scene reconstruction. Code will be public upon acceptance.