We present WayveScenes101, a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving scenes containing many dynamic and deformable elements with changing geometry and texture. The dataset comprises 101 driving scenes across a wide range of environmental conditions and driving scenarios. The dataset is designed for benchmarking reconstructions on in-the-wild driving scenes, with many inherent challenges for scene reconstruction methods including image glare, rapid exposure changes, and highly dynamic scenes with significant occlusion. Along with the raw images, we include COLMAP-derived camera poses in standard data formats. We propose an evaluation protocol for evaluating models on held-out camera views that are off-axis from the training views, specifically testing the generalisation capabilities of methods. Finally, we provide detailed metadata for all scenes, including weather, time of day, and traffic conditions, to allow for a detailed model performance breakdown across scene characteristics. Dataset and code are available at https://github.com/wayveai/wayve_scenes.

翻译：我们提出了WayveScenes101数据集，旨在帮助社区推进新颖视角合成技术的前沿，该数据集专注于包含许多具有变化几何结构和纹理的动态与可变形元素的、具有挑战性的驾驶场景。该数据集包含101个驾驶场景，涵盖了广泛的环境条件和驾驶情境。该数据集专为在真实世界驾驶场景上进行三维重建的基准测试而设计，这些场景对重建方法提出了诸多固有挑战，包括图像眩光、快速曝光变化以及存在显著遮挡的高度动态场景。除了原始图像，我们还以标准数据格式提供了通过COLMAP导出的相机位姿。我们提出了一种评估协议，用于在训练视角之外的离轴相机视角上评估模型，专门测试方法的泛化能力。最后，我们为所有场景提供了详细的元数据，包括天气、一天中的时间和交通状况，以便能够根据场景特性对模型性能进行详细分析。数据集和代码可在 https://github.com/wayveai/wayve_scenes 获取。