构建合成街景三维网格数据集，解决真实数据缺乏精确几何与法向评估的问题。
SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset
- 用CARLA模拟器生成带法向量的高精度街景三维网格
- 通过六摄像头五激光雷达虚拟驾驶采集真实感输入数据
- 为当前表面重建方法提供位置与法向双重评估基准
街景三维表面重建对数字娱乐和自动驾驶仿真至关重要。然而现有数据集如KITTI、Waymo和nuScenes仅提供噪声较大的激光点云作为几何真值，难以精确评估表面位置，且缺乏法向量数据。为此，我们提出SS3DM数据集，包含从CARLA模拟器导出的精确合成街景三维网格模型，支持表面位置与法向量的准确评估。通过在多样化户外场景中虚拟驾驶配备六台RGB相机和五台激光雷达的车辆，模拟真实驾驶场景下的输入数据。基于该数据集，我们建立了前沿表面重建方法的基准评测体系，全面评估相关技术挑战。
原文摘要 · Abstract (English)
Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise \textbf{S}ynthetic \textbf{S}treet-view \textbf{3D} \textbf{M}esh models exported from the CARLA simulator. These mesh models facilitate accurate position evaluation and include normal vectors for evaluating surface normal. To simulate the input data in realistic driving scenarios for 3D reconstruction, we virtually drive a vehicle equipped with six RGB cameras and five LiDAR sensors in diverse outdoor scenes. Leveraging this dataset, we establish a benchmark for state-of-the-art surface reconstruction methods, providing a comprehensive evaluation of the associated challenges. For more information, visit our homepage at https://ss3dm.top.
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