One Million Scenes for Autonomous Driving: ONCE Dataset
- URL: http://arxiv.org/abs/2106.11037v1
- Date: Mon, 21 Jun 2021 12:28:08 GMT
- Title: One Million Scenes for Autonomous Driving: ONCE Dataset
- Authors: Jiageng Mao, Minzhe Niu, Chenhan Jiang, Hanxue Liang, Xiaodan Liang,
Yamin Li, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Jie Yu, Hang Xu, Chunjing Xu
- Abstract summary: We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario.
The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available.
We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
- Score: 91.94189514073354
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for
Autonomous Driving [95.42203932627102]
3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians.
Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin.
Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
arXiv Detail & Related papers (2022-12-15T11:15:14Z) - aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving
with Long-Range Perception [0.0]
This dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view.
The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain.
We trained unimodal and multimodal baseline models for 3D object detection.
arXiv Detail & Related papers (2022-11-17T10:19:59Z) - Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object
Detection [58.81316192862618]
Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR.
fusing these two modalities can significantly boost the performance of 3D perception models.
We benchmark the state-of-the-art fusion methods for the first time.
arXiv Detail & Related papers (2022-05-30T09:35:37Z) - CODA: A Real-World Road Corner Case Dataset for Object Detection in
Autonomous Driving [117.87070488537334]
We introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors.
The performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR.
We experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA.
arXiv Detail & Related papers (2022-03-15T08:32:56Z) - Weakly Supervised Training of Monocular 3D Object Detectors Using Wide
Baseline Multi-view Traffic Camera Data [19.63193201107591]
7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users.
We develop an approach using a weakly supervised method of fine tuning 3D object detectors for traffic observation cameras.
Our method achieves vehicle 7DoF pose prediction accuracy on our dataset comparable to the top performing monocular 3D object detectors on autonomous vehicle datasets.
arXiv Detail & Related papers (2021-10-21T08:26:48Z) - SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous
Driving [94.11868795445798]
We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories.
To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes.
We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
arXiv Detail & Related papers (2021-06-21T13:55:57Z) - Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data
Augmentation [77.60050239225086]
We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images.
Our approach is fully automatic without any human interaction.
We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
arXiv Detail & Related papers (2020-12-15T03:03:38Z) - The NEOLIX Open Dataset for Autonomous Driving [1.4091801425319965]
We present the NEOLIX dataset and its applica-tions in the autonomous driving area.
Our dataset includes about 30,000 frames with point cloud la-bels, and more than 600k 3D bounding boxes withannotations.
arXiv Detail & Related papers (2020-11-27T02:27:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.