Efficient Depth-Guided Urban View Synthesis

Understand

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.