  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.
