ApolloScape |
2018 |
- 140,000 annotated images
- 20,000 Lidar 3D point cloud annotation data
- No radar
|
- Include GPS/IMU data/timestamps
- Different times in the day
- Mainly in Beijing, China
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D/3D boxes
|
BDD100K |
2017 |
- 120,000,000 with 100,000 annotated images (1280 * 720)
- No lidar/radar
|
- Multiple cities
- Multiple scene type
- Different times in the day
- Include GPS/IMU data/timestamps
- Multiple weather
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D boxes
|
Cityscapes |
2016 |
- 25,000 annotated images (1280 * 720)
- No lidar/radar
|
- 50 cities
- Several months
- Daytime
- Good weather conditions
- Include GPS/timestamp metadata
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D boxes
|
KITTI |
2012 |
- 15,000 annotated images (1248 * 384)
- 15,000 Lidar 3D point cloud annotation data
- No radar
|
- Include GPS/IMU data/timestamps
- Daytime
- Mainly in Karlsruhe
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D/3D boxes
|
Lyft Dataset |
2019 |
- 55,000 3D annotated images
- HD Mapping data
|
- 1,000 driving scenes in multiple cities
- Different times in the day
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D/3D boxes
|
nuScenes |
2019 |
- 1,400,000 with 40,000 annotated images
- 390,000 Lidar 3D point cloud data
- 1,400,000 radar sweeps
|
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D/3D boxes
|
Waymo Open Dataset |
2019 |
- 200,000 annotated images (1920 * 1280 & 1920 * 886)
- Lidar 3D point cloud data
- 12 million 3D labels and 1.2 million 2D labels
|
- 1,000 driving scenes in multiple cities
- Different times in the day
- Multiple weather (day and night, dawn and dusk, sun and rain)
|
- Semantic
- Instance-wise
- Dense pixel annotations
- 2D/3D boxes
|