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.

Dataset

This dataset was created by Yuyang Xia

Released under Database: Open Database, Contents: Database Contents

Contents

Dataset

This dataset was created by Yuyang Xia

Released under Database: Open Database, Contents: Database Contents

Contents

The Argoverse 1 open-source data collection includes:

A 3D Tracking Dataset with 113 3D annotated scenes A Motion Forecasting Dataset with 324,557 scenarios

The Argoverse 2 open-source data collection includes:

A Sensor Dataset with 1,000 3D annotated scenarios — each with lidar, ring camera, and stereo sensor data A Lidar Dataset with 20,000 unlabeled scenarios suitable for self-supervised learning A Motion Forecasting Dataset with 250,000 interesting driving scenarios with richer attributes than its predecessor, the Argoverse 1 Motion Forecasting Dataset A Map Change Dataset with 1,000 scenarios, 200 of which depict scenes that changed since mapping