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 quantitative and qualitative evaluations of state-of-the-art Gaussian Splatting methods across different difficulty levels. Our results show that Gaussian Splatting is 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 have released our data to help advance self-driving and urban robotics simulation technology.
逼真的模拟器在基于视觉的自动驾驶车辆（AVs）的训练和评估中至关重要，其核心能力是新视角合成（Novel View Synthesis, NVS）。NVS通过生成多样的未见视角，适应自动驾驶车辆的广泛且连续的姿态分布。近年来，诸如3D高斯点云（3D Gaussian Splatting）等辐射场技术在实时速度下实现了逼真渲染，并广泛用于大规模驾驶场景建模。然而，目前的性能评估通常基于插值设置，训练和测试视角高度相关。相比之下，外推评估（extrapolation），即测试视角与训练视角大幅偏离的情况，尚未得到充分探索，这限制了通用模拟技术的发展。 为填补这一空白，我们利用公开的自动驾驶数据集，这些数据集包含多次遍历、多辆车和多相机设置，构建了首个 外推城市视角合成基准（Extrapolated Urban View Synthesis, EUVS）。同时，我们对不同难度级别下的最新高斯点云方法进行了定量和定性评估。结果表明，高斯点云方法容易过拟合到训练视角。此外，即使结合扩散先验或改进几何处理，在大幅视角变化下也无法从根本上提升NVS性能，这凸显了对更鲁棒方法和大规模训练的需求。 我们已公开了相关数据，以推动自动驾驶与城市机器人模拟技术的进一步发展。