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

Keywords: indoor surface reconstruction, gaussian splatting

Abstract:

3D reconstruction of indoor and urban environments is a prominent research topic with various downstream applications.
However, existing geometric priors for addressing low-texture regions in indoor and urban settings often lack global consistency.
Moreover, Gaussian Splatting and implicit SDF fields often suffer from discontinuities or exhibit computational inefficiencies, resulting in a loss of detail. 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 the proposed novel implicit-structured GS representations provide smoothness without sacrificing efficiency and high-frequency details. Specifically, we propose a semantic GS representation to predict the probability of all semantic regions and deploy a structure plane regularization with learnable plane indicators for 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.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 15200