  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.
