WayveScenes101, a dataset designed to help the community advance the latest technologies for novel view synthesis, focuses on challenging driving scenarios that contain many dynamic and deformable elements with ever-changing geometries and textures.
The dataset contains 101 driving scenarios, covering various environmental conditions and driving scenarios. The dataset is specifically designed for benchmark reconstruction of wild driving scenes, and scene reconstruction methods face many inherent challenges, including image glare, rapid exposure changes, and highly dynamic scenes with significant occlusion.
In addition to the raw images, camera poses derived from COLMAP are also included in a standard data format.
An evaluation protocol is proposed to evaluate models on retained camera views off-axis from the training views, especially to test the generalization capabilities of the method.
Finally, detailed metadata is provided for all scenarios, including weather, time of day, and traffic conditions, to allow detailed model performance segmentation across scenario characteristics. The dataset and code can be obtained from this URL.
Perception Dataset
1950 autonomous driving video clips, each video including 20 seconds of continuous driving footage
Four types of labels for cars, pedestrians, bicycles, and traffic signs
12.6 million 3D frames, 11.8 million 2D frames
Sensor data: 1 medium-range lidar, 4 short-range lidar, 5 cameras
The collection covers downtown and suburbs in Phoenix, Kirkland, Mountain View, and San Francisco in California. It also involves data under various driving conditions, including day, night, dawn, dusk, rainy days, and sunny days
Motion Dataset
Including 574 hours of data, 103,354 data fragments with maps
There are three types of labels: car, pedestrian, and bicycle, and each object is marked with a 2D box.
Mining behaviors and scenarios for behavioral prediction research, including turning, merging, lane changes, and intersections
Locations include: San Francisco, Phoenix, Mountain View, Los Angeles, Detroit and Seattle
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ARXIV：https://arxiv.org/abs/2407.08280
Download address:https://waymo.com/open/
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