标题： ExtraGS：具有不确定性引导生成先验的几何感知轨迹外推 显示英文标题

标题： ExtraGS: Geometric-Aware Trajectory Extrapolation with Uncertainty-Guided Generative Priors

摘要： 从记录的驾驶日志中合成外推视图对于模拟自动驾驶车辆的驾驶场景至关重要，但仍然是一个具有挑战性的任务。 最近的方法利用生成先验作为伪真实数据，但通常会导致几何一致性较差和渲染过于平滑。 为了解决这些限制，我们提出了ExtraGS，这是一个整合几何和生成先验的整体轨迹外推框架。 ExtraGS的核心是一种基于混合高斯-符号距离函数（SDF）设计的新道路表面高斯（RSG）表示，以及使用可学习缩放因子来高效处理远处物体的远场高斯（FFG）。 此外，我们开发了一个基于球面谐波的自监督不确定性估计框架，该框架仅在出现外推伪影时选择性地集成生成先验。 在多个数据集、多样化的多摄像头设置和各种生成先验上的广泛实验表明，ExtraGS显著提高了外推视图的真实感和几何一致性，同时保留了原始轨迹上的高保真度。 显示英文摘要

摘要： Synthesizing extrapolated views from recorded driving logs is critical for simulating driving scenes for autonomous driving vehicles, yet it remains a challenging task. Recent methods leverage generative priors as pseudo ground truth, but often lead to poor geometric consistency and over-smoothed renderings. To address these limitations, we propose ExtraGS, a holistic framework for trajectory extrapolation that integrates both geometric and generative priors. At the core of ExtraGS is a novel Road Surface Gaussian(RSG) representation based on a hybrid Gaussian-Signed Distance Function (SDF) design, and Far Field Gaussians (FFG) that use learnable scaling factors to efficiently handle distant objects. Furthermore, we develop a self-supervised uncertainty estimation framework based on spherical harmonics that enables selective integration of generative priors only where extrapolation artifacts occur. Extensive experiments on multiple datasets, diverse multi-camera setups, and various generative priors demonstrate that ExtraGS significantly enhances the realism and geometric consistency of extrapolated views, while preserving high fidelity along the original trajectory.