Extrapolated Urban View Synthesis Benchmark

TL;DR

This paper introduces the first benchmark for extrapolated urban view synthesis, revealing current methods' limitations in generalizing to large view changes and emphasizing the need for more robust approaches.

Contribution

It creates a new benchmark for extrapolated view synthesis in urban scenes and evaluates state-of-the-art methods, highlighting their overfitting and limited generalization.

Findings

Current NVS methods overfit to training views.

Diffusion priors and geometry improvements do not solve large view change issues.

Large-scale training and robust approaches are needed.

Abstract

Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs). At their core is Novel View Synthesis (NVS), a crucial capability that generates diverse unseen viewpoints to accommodate the broad and continuous pose distribution of AVs. Recent advances in radiance fields, such as 3D Gaussian Splatting, achieve photorealistic rendering at real-time speeds and have been widely used in modeling large-scale driving scenes. However, their performance is commonly evaluated using an interpolated setup with highly correlated training and test views. In contrast, extrapolation, where test views largely deviate from training views, remains underexplored, limiting progress in generalizable simulation technology. To address this gap, we leverage publicly available AV datasets with multiple traversals, multiple vehicles, and multiple cameras to build the first Extrapolated Urban View Synthesis (EUVS) benchmark. Meanwhile, we conduct both quantitative and qualitative evaluations of state-of-the-art NVS methods across different evaluation settings. Our results show that current NVS methods are prone to overfitting to training views. Besides, incorporating diffusion priors and improving geometry cannot fundamentally improve NVS under large view changes, highlighting the need for more robust approaches and large-scale training. We will release the data to help advance self-driving and urban robotics simulation technology.