  Recent advances in implicit scene representation enable high-fidelity street
view novel view synthesis. However, existing methods optimize a neural radiance
field for each scene, relying heavily on dense training images and extensive
computation resources. To mitigate this shortcoming, we introduce a new method
called Efficient Depth-Guided Urban View Synthesis (EDUS) for fast feed-forward
inference and efficient per-scene fine-tuning. Different from prior
generalizable methods that infer geometry based on feature matching, EDUS
leverages noisy predicted geometric priors as guidance to enable generalizable
urban view synthesis from sparse input images. The geometric priors allow us to
apply our generalizable model directly in the 3D space, gaining robustness
across various sparsity levels. Through comprehensive experiments on the
KITTI-360 and Waymo datasets, we demonstrate promising generalization abilities
on novel street scenes. Moreover, our results indicate that EDUS achieves
state-of-the-art performance in sparse view settings when combined with fast
test-time optimization.
