One Million Scenes for Autonomous Driving: ONCE Dataset

By Jiageng Mao, Minzhe Niu, Chenhan Jiang, Hanxue Liang, Xiaodan Liang, Yamin Li, Chao Ye, Wei Zhang, Zhenguo Li, Jie Yu and 2 more. NeurIPS Datasets and Benchmarks, 2021

383 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ddbd9baf007d86f6174b2d41c36fda1c5ec64b58

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Citations in the corpus

Voxel Transformer for 3D Object Detection (2021, methodology): “Alternatively, voxel-based approaches [44, 34, 38, 16, 37] first rasterize point clouds into voxels and apply 3D convolutional networks to extract voxel features, and then voxels are transformed into a Bird-Eye-View (BEV) feature map and 3D boxes are generated on the BEV map.”
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (2022, neutral): “Evaluation on ONCE Dataset.”
Point2Seq: Detecting 3D Objects as Sequences (2022, methodology): “Data augmentations are kept the same as [17].”
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection (2022, neutral): “…real single vehicle 1M 200k 12M 4 ApolloScape [12] 2018 real single vehicle 144k 0 70k 8-35 BBD100K [30] 2020 real single vehicle 100M 0 0 10 ONCE [17] 2021 real single vehicle 7M 1M 417k 5 SYNTHIA [19] 2016 simulated single vehicle 213k 0 - 13 V2X-Sim [16] 2021 simulated multi-vehicle 0 10k…”
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023, methodology): “Unlike ONCE, our dataset contains HD maps and high frame rate lidar.”