用四阶段方法实现更真实的街景视角外推，提升自动驾驶仿真效果
ViSE: A Systematic Approach to Vision-Only Street-View Extrapolation
- 分四步走：伪LiDAR初始化、2D-SDF建模路面、生成伪真值监督、去除时间伪影
- 在RealADSim-NVS上得分0.441，排名第一，显著优于其他方法
- 适合自动驾驶仿真、视觉外推研究者使用
真实场景视角外推对自动驾驶闭环仿真至关重要，但现有新型视图合成（NVS）方法常在原始轨迹外产生失真和不一致图像。本文提出在ICCV 2025 RealADSim Workshop NVS赛道中夺冠的解决方案，采用四阶段全流程方法：首先通过数据驱动初始化生成鲁棒的伪LiDAR点云，避免局部最优；其次引入强几何先验，用新型降维隐式表面函数（2D-SDF）建模道路表面；第三，利用生成先验为外推视角创建伪真值，提供辅助监督；最后，通过数据驱动适配网络消除时间特异性伪影。在RealADSim-NVS基准测试中，本方法最终得分为0.441，位列所有参赛者第一。
原文摘要 · Abstract (English)
Realistic view extrapolation is critical for closed-loop simulation in autonomous driving, yet it remains a significant challenge for current Novel View Synthesis (NVS) methods, which often produce distorted and inconsistent images beyond the original trajectory. This report presents our winning solution which ctook first place in the RealADSim Workshop NVS track at ICCV 2025. To address the core challenges of street view extrapolation, we introduce a comprehensive four-stage pipeline. First, we employ a data-driven initialization strategy to generate a robust pseudo-LiDAR point cloud, avoiding local minima. Second, we inject strong geometric priors by modeling the road surface with a novel dimension-reduced SDF termed 2D-SDF. Third, we leverage a generative prior to create pseudo ground truth for extrapolated viewpoints, providing auxilary supervision. Finally, a data-driven adaptation network removes time-specific artifacts. On the RealADSim-NVS benchmark, our method achieves a final score of 0.441, ranking first among all participants.
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