This supports a point cloud forecasting task in which the future frames of the test set serve as the ground truth. We’ve divided the dataset into train, validation, and test sets of 16,000, 2,000, and 2,000 scenarios. The dataset is designed to support research into self-supervised learning in the lidar domain, as well as point cloud forecasting. It does not include imagery or 3D annotations. The Argoverse 2 Lidar Dataset is a collection of 20,000 scenarios with lidar sensor data, HD maps, and ego-vehicle pose. When referencing these datasets or any of the materials we provide, please use the following , The Argoverse 2 datasets are described in our publications, Argoverse 2 and Trust, but Verify, in the NeurIPS 2021 dataset track. Argoverse 2 datasets also share a common API, which allows users to easily access and visualize the data and maps. Argoverse 2 Map Change Dataset: contains 1,000 scenarios, 200 of which depict real-world HD map changesĪrgoverse 2 datasets share a common HD map format that is richer than the HD maps in Argoverse 1.Argoverse 2 Lidar Dataset: contains 20,000 unannotated lidar sequences.This dataset improves upon the Argoverse 1 Motion Forecasting Dataset. Argoverse 2 Motion Forecasting Dataset: contains 250,000 scenarios with trajectory data for many object types.This dataset improves upon the Argoverse 1 3D Tracking dataset. Argoverse 2 Sensor Dataset: contains 1,000 3D annotated scenarios with lidar, stereo imagery, and ring camera imagery.This release builds upon the initial launch of Argoverse (“Argoverse 1”), which was among the first data releases of its kind to include HD maps for machine learning and computer vision research.Īrgoverse 2 includes four open-source datasets: cities: Austin, Detroit, Miami, Pittsburgh, Palo Alto, and Washington, D.C. Argoverse 2 is a collection of open-source autonomous driving data and high-definition (HD) maps from six U.S.

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