The Argoverse 2 Lidar Dataset is a collection of 20,000 scenarios with lidar sensor data, HD maps, and ego-vehicle pose. It does not include imagery or 3D annotations. The dataset is designed to support research into self-supervised learning in the lidar domain, as well as point cloud forecasting.

The dataset is divided into train, validation, and test sets of 16,000, 2,000, and 2,000 scenarios. This supports a point cloud forecasting task in which the future frames of the test set serve as the ground truth. Nonetheless, we encourage the community to use the dataset broadly for other tasks, such as self-supervised learning and map automation.

All Argoverse datasets contain lidar data from two out-of-phase 32 beam sensors rotating at 10 Hz. While this can be aggregated into 64 beam frames at 10 Hz, it is also reasonable to think of this as 32 beam frames at 20 Hz. Furthermore, all Argoverse datasets contain raw lidar returns with per-point timestamps, so the data does not need to be interpreted in quantized frames.