  Synthesizing novel views for urban environments is crucial for tasks like
autonomous driving and virtual tours. Compared to object-level or indoor
situations, outdoor settings present unique challenges, such as inconsistency
across frames due to moving vehicles and camera pose drift over lengthy
sequences. In this paper, we introduce a method that tackles these challenges
on view synthesis for outdoor scenarios. We employ a neural point light field
scene representation and strategically detect and mask out dynamic objects to
reconstruct novel scenes without artifacts. Moreover, we simultaneously
optimize camera pose along with the view synthesis process, and thus, we
simultaneously refine both elements. Through validation on real-world urban
datasets, we demonstrate state-of-the-art results in synthesizing novel views
of urban scenes.
