Argoverse: 3D Tracking and Forecasting With Rich Maps
Georgia Institute of Technology · Carnegie Mellon University
Abstract
We present Argoverse, a dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps. The sensor data consists of 360 degree images from 7 cameras with overlapping fields of view, forward-facing stereo imagery, 3D point clouds from long range LiDAR, and 6-DOF pose. Our 290km of mapped lanes contain rich geometric and semantic metadata which are not currently available in any public dataset. All data is released under a Creative Commons license at…
Citation impact
- FWCI
- 72.30
- Percentile
- 100%
- References
- 56
Authors
11- MCMing-Fang ChangCorresponding
Georgia Institute of Technology, Carnegie Mellon University
- JLJohn Lambert
Georgia Institute of Technology, Carnegie Mellon University
- PSPatsorn Sangkloy
Georgia Institute of Technology
- JSJagjeet Singh
Georgia Institute of Technology, Carnegie Mellon University
- SBSławomir Bąk
Georgia Institute of Technology
Topics & keywords
- Computer science
- Artificial intelligence
- Computer vision
- Point cloud
- Trajectory
- Tracking (education)
- Benchmark (surveying)
- Lidar