SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
Mingrui Li, Shuhong Liu, Heng Zhou, Guohao Zhu, Na Cheng, Tianchen Deng, Hongyu Wang*
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."
BibTeX
@inproceedings{eccv2024_sgsslamsemanticg,
title = {SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM},
author = {Mingrui Li and Shuhong Liu and Heng Zhou and Guohao Zhu and Na Cheng and Tianchen Deng and Hongyu Wang*},
booktitle = {ECCV 2024},
year = {2024}
}