WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
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.
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Jianhao Zheng
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2025-04-13T19:25:45.871Z
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Jianhao
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Jianhao97
67f78d2850c25afaf8a12110
Zihan Zhu
claimed_verified
2025-04-13T19:25:43.907Z
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ZIHAN ZHU
user
zzh2000
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Valentin Bieri
admin_assigned
2025-04-10T13:28:57.349Z
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Valentin Bieri
user
bieriv
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Marc Pollefeys
admin_assigned
2025-04-10T13:28:45.819Z
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Marc Pollefeys
user
mapo1
67f78d2850c25afaf8a12113
Songyou Peng
admin_assigned
2025-04-10T13:28:37.970Z
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Songyou Peng
user
syp
67f78d2850c25afaf8a12114
Iro Armeni
admin_assigned
2025-04-10T13:28:29.907Z
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Iro Armeni
user
ir0
WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
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.
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.
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