SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
<|reference_start|>SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM: 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.<|reference_end|>
@article{li2024sgs-slam:,
    title={SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM},
    author={Mingrui Li, Shuhong Liu, Heng Zhou, Guohao Zhu, Na Cheng, Tianchen
  Deng, Hongyu Wang},
    journal={European Conference on Computer Vision (ECCV) 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03246},
    primaryClass={cs.CV cs.AI cs.RO}
}