The task of separating dynamic objects from static environments using NeRFs
has been widely studied in recent years. However, capturing large-scale scenes
still poses a challenge due to their complex geometric structures and
unconstrained dynamics. Without the help of 3D motion cues, previous methods
often require simplified setups with slow camera motion and only a few/single
dynamic actors, leading to suboptimal solutions in most urban setups. To
overcome such limitations, we present RoDUS, a pipeline for decomposing static
and dynamic elements in urban scenes, with thoughtfully separated NeRF models
for moving and non-moving components. Our approach utilizes a robust
kernel-based initialization coupled with 4D semantic information to selectively
guide the learning process. This strategy enables accurate capturing of the
dynamics in the scene, resulting in reduced artifacts caused by NeRF on
background reconstruction, all by using self-supervision. Notably, experimental
evaluations on KITTI-360 and Pandaset datasets demonstrate the effectiveness of
our method in decomposing challenging urban scenes into precise static and
dynamic components.

使用 RoDUS 分解算法和 NeRF 模型，在城市场景中准确地捕捉静态和动态元素以及减少背景重建中的错误。