SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM

• URL: http://arxiv.org/abs/2402.03246v5
• Date: Tue, 26 Mar 2024 12:35:03 GMT
• Title: SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
• Authors: Mingrui Li, Shuhong Liu, Heng Zhou, Guohao Zhu, Na Cheng, Tianchen Deng, Hongyu Wang,
• Abstract summary: We present SGS-SLAM, the first semantic visual SLAM system based on Splatting. It appearance geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems. It delivers state-of-the-art performance in camera pose estimation, map reconstruction, precise semantic segmentation, and object-level geometric accuracy.
• Score: 5.144010652281121
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understanding, and object-level geometry. We introduce a unique semantic feature loss that effectively compensates for the shortcomings of traditional depth and color losses in object optimization. Through a semantic-guided keyframe selection strategy, we prevent erroneous reconstructions caused by cumulative errors. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, precise semantic segmentation, and object-level geometric accuracy, while ensuring real-time rendering capabilities.

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