title: WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
summary: We present WildGS-SLAM, a robust and efficient monocular RGB SLAM system
  designed to handle dynamic environments by leveraging uncertainty-aware
  geometric mapping. Unlike traditional SLAM systems, which assume static scenes,
  our approach integrates depth and uncertainty information to enhance tracking,
  mapping, and rendering performance in the presence of moving objects. We
  introduce an uncertainty map, predicted by a shallow multi-layer perceptron and
  DINOv2 features, to guide dynamic object removal during both tracking and
  mapping. This uncertainty map enhances dense bundle adjustment and Gaussian map
  optimization, improving reconstruction accuracy. Our system is evaluated on
  multiple datasets and demonstrates artifact-free view synthesis. Results
  showcase WildGS-SLAM's superior performance in dynamic environments compared to
  state-of-the-art methods.
paper_authors._id: 67f78d2850c25afaf8a1210f
paper_authors.name: Jianhao Zheng
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paper_authors.fullname: Jianhao
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paper_authors.name: Zihan Zhu
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paper_authors.fullname: ZIHAN ZHU
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paper_authors.user: zzh2000
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paper_authors.name: Valentin Bieri
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paper_authors.fullname: Valentin Bieri
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paper_authors.user: bieriv
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paper_authors.name: Marc Pollefeys
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paper_authors.fullname: Marc Pollefeys
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paper_authors.user: mapo1
paper_authors._id: 67f78d2850c25afaf8a12113
paper_authors.name: Songyou Peng
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paper_authors.fullname: Songyou Peng
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paper_authors.user: syp
paper_authors._id: 67f78d2850c25afaf8a12114
paper_authors.name: Iro Armeni
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paper_authors.avatarUrl: /avatars/882f56565b4ebfabf1c13e199d74a4de.svg
paper_authors.fullname: Iro Armeni
paper_authors.type: user
paper_authors.user: ir0
paper_title: WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
paper_summary: We present WildGS-SLAM, a robust and efficient monocular RGB SLAM system
  designed to handle dynamic environments by leveraging uncertainty-aware
  geometric mapping. Unlike traditional SLAM systems, which assume static scenes,
  our approach integrates depth and uncertainty information to enhance tracking,
  mapping, and rendering performance in the presence of moving objects. We
  introduce an uncertainty map, predicted by a shallow multi-layer perceptron and
  DINOv2 features, to guide dynamic object removal during both tracking and
  mapping. This uncertainty map enhances dense bundle adjustment and Gaussian map
  optimization, improving reconstruction accuracy. Our system is evaluated on
  multiple datasets and demonstrates artifact-free view synthesis. Results
  showcase WildGS-SLAM's superior performance in dynamic environments compared to
  state-of-the-art methods.
paper_ai_summary: A robust monocular RGB SLAM system integrates uncertainty-aware geometric mapping using DINOv2 features and a shallow multi-layer perceptron to improve tracking, mapping, and rendering in dynamic environments.
paper_ai_keywords: monocular RGB SLAM uncertainty-aware geometric mapping depth information uncertainty map shallow multi-layer perceptron DINOv2 features dynamic object removal dense bundle adjustment Gaussian map optimization view synthesis