<p>Implicit neural representation has demonstrated promising results in 3D reconstruction in various scenes. However, existing approaches either struggle to model fast-moving objects or are incapable of handling large-scale camera ego-motion in urban environments. This leads to low-quality synthesized views of the large-scale urban scenes. In this paper, we aim to jointly solve the problems caused by large-scale scenes and fast-moving vehicles, which are more practical and challenging. To this end, we propose a progressive scene graph network architecture to learn the local scene representations of dynamic objects and global urban scenes. The progressive learning architecture dynamically allocates a new local scene graph trained on frames within a temporal window, with the window size automatically determined, allowing us to scale up the representation to large-scale scenes. Besides, according to our observations, fast-moving objects are observed only in a few frames, which leads to a significant decline in reconstruction accuracy for dynamic objects. Therfore, We introduce a foundation-guided object representation that extracts object-centric visual priors and conditions the density and color decoders in normalized object coordinates. We further propose a frequency-progressive regularization strategy that gradually exposes high-frequency positional, directional, and pose encodings during training, reducing overfitting to sparse object observations. Experimental results demonstrate that our method achieves state-of-the-art view synthesis accuracy, object manipulation, and scene roaming ability in various scenes. The code will be open-sourced on https://github.com/dtc111111/prosgnerf.</p>