AtlasGS: Atlanta-world Guided Surface Reconstruction with Implicit Structured Gaussians
Xiyu Zhang, Chong Bao, YiPeng Chen, Hongjia Zhai, Yitong Dong, Hujun Bao, Zhaopeng Cui, Guofeng Zhang
3D reconstruction of indoor and urban environments is a prominent research topic with various downstream applications. However, current geometry prior to address low-texture regions in indoor and urban is not globally consistent.Besides, Gaussian Splatting (GS) or implicit SDF field either suffer from discontinuities or are computationally inefficient with fewer details.To address these issues, we propose an Atlanta-world guided implicit-structured Gaussian Splatting that achieves smooth indoor and urban scene reconstruction while preserving high-frequency details and rendering efficiency.By leveraging the Atlanta-world model, we ensure the accurate surface reconstruction for low-texture regions, while proposed novel implicit-structured GS representations provide smoothness without sacrificing efficiency and high-frequency details.Specifically, we propose a semantic GS representation to predict probability of all semantic regions and deploy a structure plane regularization with learnable plane indicators to global accurate surface reconstruction.Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in both indoor and urban scenes, delivering superior surface reconstruction quality.