WildGS-SLAM：动态环境中的单目高斯泼溅SLAM
WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
AI总结：
WildGS-SLAM是一种单目RGB SLAM系统，通过深度和不确定性信息结合DINOv2特征预测的不确定性图指导动态物体移除，增强跟踪、映射与渲染，在动态环境中实现无伪影视图合成且优于现有方法。
摘要：
我们提出了WildGS-SLAM，一个鲁棒高效的单目RGB SLAM系统，通过利用不确定性感知的几何映射来处理动态环境。不同于假设静态场景的传统SLAM系统，我们的方法整合深度和不确定性信息，以在有移动物体时增强跟踪、映射和渲染性能。我们引入了一个由浅层多层感知机和DINOv2特征预测的不确定性图，以在跟踪和映射期间指导动态物体移除。该不确定性图增强了密集束调整和高斯图优化，提升了重建精度。我们的系统在多个数据集上进行了评估，展示了无伪影的视图合成。结果表明，WildGS-SLAM在动态环境中表现出优于最先进方法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
