In this work, we introduce \textbf{XSIM}, a sensor simulation framework for autonomous driving. XSIM extends 3DGUT splatting with a generalized rolling-shutter modeling tailored for autonomous driving applications. Our framework provides a unified and flexible formulation for appearance and geometric sensor modeling, enabling rendering of complex sensor distortions in dynamic environments. We identify spherical cameras, such as LiDARs, as a critical edge case for existing 3DGUT splatting due to cyclic projection and time discontinuities at azimuth boundaries leading to incorrect particle projection. To address this issue, we propose a phase modeling mechanism that explicitly accounts temporal and shape discontinuities of Gaussians projected by the Unscented Transform at azimuth borders. In addition, we introduce an extended 3D Gaussian representation that incorporates two distinct opacity parameters to resolve mismatches between geometry and color distributions. As a result, our framework provides enhanced scene representations with improved geometric consistency and photorealistic appearance. We evaluate our framework extensively on multiple autonomous driving datasets, including Waymo Open Dataset, Argoverse 2, and PandaSet. Our framework consistently outperforms strong recent baselines and achieves state-of-the-art performance across all datasets. The source code is publicly available at \href{https://github.com/whesense/XSIM}{https://github.com/whesense/XSIM}.

翻译：本文提出了一种用于自动驾驶的传感器仿真框架——**XSIM**。该框架在3DGUT点云渲染技术基础上，扩展了专门为自动驾驶应用设计的广义卷帘快门模型。我们的框架为外观与几何传感器建模提供了统一且灵活的表述，能够渲染动态环境中的复杂传感器畸变。我们指出，球形相机（如激光雷达）是现有3DGUT点云渲染技术的关键边界案例，其循环投影特性和方位边界处的时间不连续性会导致粒子投影错误。为解决此问题，我们提出了一种相位建模机制，该机制显式地处理了由无迹变换在方位边界处投影的高斯分布所产生的时间和形状不连续性。此外，我们引入了一种扩展的3D高斯表示方法，该方法包含两个独立的不透明度参数，以解决几何与颜色分布之间的不匹配问题。因此，我们的框架提供了增强的场景表示，具有更好的几何一致性和逼真的外观表现。我们在多个自动驾驶数据集（包括Waymo开放数据集、Argoverse 2和PandaSet）上对框架进行了全面评估。我们的框架在所有数据集上均持续超越近期强基线方法，并取得了最先进的性能。源代码已公开于 \href{https://github.com/whesense/XSIM}{https://github.com/whesense/XSIM}。