  Current perception models in autonomous driving have become notorious for
greatly relying on a mass of annotated data to cover unseen cases and address
the long-tail problem. On the other hand, learning from unlabeled large-scale
collected data and incrementally self-training powerful recognition models have
received increasing attention and may become the solutions of next-generation
industry-level powerful and robust perception models in autonomous driving.
However, the research community generally suffered from data inadequacy of
those essential real-world scene data, which hampers the future exploration of
fully/semi/self-supervised methods for 3D perception. In this paper, we
introduce the ONCE (One millioN sCenEs) dataset for 3D object detection in the
autonomous driving scenario. The ONCE dataset consists of 1 million LiDAR
scenes and 7 million corresponding camera images. The data is selected from 144
driving hours, which is 20x longer than the largest 3D autonomous driving
dataset available (e.g. nuScenes and Waymo), and it is collected across a range
of different areas, periods and weather conditions. To facilitate future
research on exploiting unlabeled data for 3D detection, we additionally provide
a benchmark in which we reproduce and evaluate a variety of self-supervised and
semi-supervised methods on the ONCE dataset. We conduct extensive analyses on
those methods and provide valuable observations on their performance related to
the scale of used data. Data, code, and more information are available at
https://once-for-auto-driving.github.io/index.html.
