Freeze-Frame With StaticNeRF: Uncertainty-Guided NeRF Map Reconstruction in Dynamic Scenes

초록

Recent advances in neural representations have shown great promise for enabling high-fidelity dense mapping in robotics. Given the inherently dynamic nature of real-world environments, many studies have attempted to learn static scene representations from dynamic observations. However, existing methods often fail to remove subtly moving objects and struggle to accurately recover occluded static backgrounds, which leads to critical limitations in practice. Furthermore, when static neural maps are used for localization, dynamic content in query images must be handled effectively. To overcome these challenges, we propose a static neural mapping framework that is robust to diverse dynamic environments and capable of processing dynamic content during localization. We evaluated our approach through extensive experiments on both public and in-house datasets. Our method improves both dynamic object removal and localization robustness under dynamic conditions, and constitutes a significant step toward resilient robot navigation in real-world environments. © 2016 IEEE.

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