Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic
scenes often involve explicit modeling of scene dynamics. However, this
approach faces challenges in modeling scene dynamics in urban environments,
where moving objects of various categories and scales are present. In such
settings, it becomes crucial to effectively eliminate moving objects to
accurately reconstruct static backgrounds. Our research introduces an
innovative method, termed here as Entity-NeRF, which combines the strengths of
knowledge-based and statistical strategies. This approach utilizes entity-wise
statistics, leveraging entity segmentation and stationary entity classification
through thing/stuff segmentation. To assess our methodology, we created an
urban scene dataset masked with moving objects. Our comprehensive experiments
demonstrate that Entity-NeRF notably outperforms existing techniques in
removing moving objects and reconstructing static urban backgrounds, both
quantitatively and qualitatively.

实体 - 神经辐射场（Entity-NeRF）是一种结合了基于知识和统计策略的创新方法，通过利用实体分割和静态实体分类来消除移动物体并准确重建静态城市背景。在移动对象遮罩的城市场景数据集上进行的综合实验表明，Entity-NeRF 在定量和定性上明显优于现有技术。