UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction

• URL: http://arxiv.org/abs/2412.03473v2
• Date: Fri, 21 Mar 2025 10:30:57 GMT
• Title: UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction
• Authors: Ziwen Li, Jiaxin Huang, Runnan Chen, Yunlong Che, Yandong Guo, Tongliang Liu, Fakhri Karray, Mingming Gong,
• Abstract summary: UrbanGS uses 2D semantic maps and an existing dynamic Gaussian approach to distinguish static objects from the scene.<n>For potentially dynamic objects, we aggregate temporal information using learnable time embeddings.<n>Our approach outperforms state-of-the-art methods in reconstruction quality and efficiency.
• Score: 86.4386398262018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing urban scenes is challenging due to their complex geometries and the presence of potentially dynamic objects. 3D Gaussian Splatting (3DGS)-based methods have shown strong performance, but existing approaches often incorporate manual 3D annotations to improve dynamic object modeling, which is impractical due to high labeling costs. Some methods leverage 4D Gaussian Splatting (4DGS) to represent the entire scene, but they treat static and dynamic objects uniformly, leading to unnecessary updates for static elements and ultimately degrading reconstruction quality. To address these issues, we propose UrbanGS, which leverages 2D semantic maps and an existing dynamic Gaussian approach to distinguish static objects from the scene, enabling separate processing of definite static and potentially dynamic elements. Specifically, for definite static regions, we enforce global consistency to prevent unintended changes in dynamic Gaussian and introduce a K-nearest neighbor (KNN)-based regularization to improve local coherence on low-textured ground surfaces. Notably, for potentially dynamic objects, we aggregate temporal information using learnable time embeddings, allowing each Gaussian to model deformations over time. Extensive experiments on real-world datasets demonstrate that our approach outperforms state-of-the-art methods in reconstruction quality and efficiency, accurately preserving static content while capturing dynamic elements.

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