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  1. README.md +103 -3
  2. metafile.yaml +21 -0
  3. raw/cipo.zip +3 -0
  4. raw/images_training_0.tar +3 -0
  5. raw/images_training_1.tar +3 -0
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  20. raw/images_validation_0.tar +3 -0
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  24. raw/lane3d_1000_v1.2.zip +3 -0
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README.md CHANGED
@@ -1,3 +1,103 @@
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- ---
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- license: cc-by-nc-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
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+ <html lang="en">
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+ <head>
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+ <meta charset="UTF-8">
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+ <title>Image Container</title>
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+ <style>
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+ .image-container {
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+ width: 845px;
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+ height: 500px;
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+ background-image: url('https://cdn.opendatalab.com/OpenLane/cover/OpenLane_01.png?x-oss-process=image/resize,l_800');
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+ background-size: cover;
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+ background-repeat: no-repeat;
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+ background-position: center center;
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+ height: 200px;
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+ }
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+ </style>
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+ </head>
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+ <body>
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+ <div class="image-container"></div>
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+ </body>
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+ </html>
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+
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+ ## 简介
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+ OpenLane 是迄今为止第一个真实世界和规模最大的 3D 车道数据集。我们的数据集从公共感知数据集 Waymo Open Dataset 中收集有价值的内容，并为 1000 个路段提供车道和最近路径对象（CIPO）注释。简而言之，OpenLane 拥有 200K 帧和超过 880K 仔细注释的车道。我们公开发布了 OpenLane 数据集，以帮助研究界在 3D 感知和自动驾驶技术方面取得进步。
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+ ## 类定义
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+ ```
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+ Lane Annotation
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+
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+ Lane shape. Each 2D/3D lane is presented as a set of 2D/3D points.
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+ Lane category. Each lane has a category such as double yellow line or curb.
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+ Lane property. Some of lanes have a property such as right, left.
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+ Lane tracking ID. Each lane except curb has a unique id.
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+ Stopline and curb.
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+
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+ CIPO/Scenes Annotation
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+
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+ 2D bounding box with a category representing the importance level of object.
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+ Scene Tag. It describes in which scenario this frame is collected.
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+ Weather Tag. It describes under what weather this frame is collected.
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+ Hours Tag. It annotates in what time this frame is collected.
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+ ```
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+ ## 数据统计
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+ ![](https://github.com/ztayty/ztayty.github.io/blob/main/image/OpenLane%E6%95%B0%E6%8D%AE%E7%BB%9F%E8%AE%A1%E5%9B%BE.png?raw=true)
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+
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+ ## 引文
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+ ```
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+ @article{chen2022persformer,
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+ title={PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark},
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+ author={Chen, Li and Sima, Chonghao and Li, Yang and Zheng, Zehan and Xu, Jiajie and Geng, Xiangwei and Li, Hongyang and He, Conghui and Shi, Jianping and Qiao, Yu and Yan, Junchi},
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+ journal={arXiv preprint arXiv:2203.11089},
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+ year={2022}
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+ }
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+ @inproceedings{Sun_2020_CVPR,
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+ author = {Sun, Pei and Kretzschmar, Henrik and Dotiwalla, Xerxes and Chouard, Aurelien and Patnaik, Vijaysai and Tsui, Paul and Guo, James and Zhou, Yin and Chai, Yuning and Caine, Benjamin and Vasudevan, Vijay and Han, Wei and Ngiam, Jiquan and Zhao, Hang and Timofeev, Aleksei and Ettinger, Scott and Krivokon, Maxim and Gao, Amy and Joshi, Aditya and Zhang, Yu and Shlens, Jonathon and Chen, Zhifeng and Anguelov, Dragomir},
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+ title = {Scalability in Perception for Autonomous Driving: Waymo Open Dataset},
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+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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+ month = {June},
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+ year = {2020}
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+ }
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+ ```
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+ ---
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+ <div>
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+
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+ ## Introduction
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+ OpenLane is the first real-world and the largest scaled 3D lane dataset to date. Our dataset collects valuable contents from public perception dataset Waymo Open Dataset and provides lane&closest-in-path object(CIPO) annotation for 1000 segments. In short, OpenLane owns 200K frames and over 880K carefully annotated lanes. We have released the OpenLane Dataset publicly to aid the research community in making advancements in 3D perception and autonomous driving technology.
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+
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+ ## Class Definitions
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+ Lane Annotation
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+
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+ Lane shape. Each 2D/3D lane is presented as a set of 2D/3D points.
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+ Lane category. Each lane has a category such as double yellow line or curb.
72
+ Lane property. Some of lanes have a property such as right, left.
73
+ Lane tracking ID. Each lane except curb has a unique id.
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+ Stopline and curb.
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+
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+ CIPO/Scenes Annotation
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+
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+ 2D bounding box with a category representing the importance level of object.
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+ Scene Tag. It describes in which scenario this frame is collected.
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+ Weather Tag. It describes under what weather this frame is collected.
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+ Hours Tag. It annotates in what time this frame is collected.
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+
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+ ## Data Overview
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+ ![](https://github.com/ztayty/ztayty.github.io/blob/main/image/OpenLane-overview.png?raw=true)
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+
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+
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+ ## Citation
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+ Please quote the following citation when citing the dataset.
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+ ```
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+ @article{chen2022persformer,
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+ title={PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark},
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+ author={Chen, Li and Sima, Chonghao and Li, Yang and Zheng, Zehan and Xu, Jiajie and Geng, Xiangwei and Li, Hongyang and He, Conghui and Shi, Jianping and Qiao, Yu and Yan, Junchi},
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+ journal={arXiv preprint arXiv:2203.11089},
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+ year={2022}
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+ }
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+ @inproceedings{Sun_2020_CVPR,
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+ author = {Sun, Pei and Kretzschmar, Henrik and Dotiwalla, Xerxes and Chouard, Aurelien and Patnaik, Vijaysai and Tsui, Paul and Guo, James and Zhou, Yin and Chai, Yuning and Caine, Benjamin and Vasudevan, Vijay and Han, Wei and Ngiam, Jiquan and Zhao, Hang and Timofeev, Aleksei and Ettinger, Scott and Krivokon, Maxim and Gao, Amy and Joshi, Aditya and Zhang, Yu and Shlens, Jonathon and Chen, Zhifeng and Anguelov, Dragomir},
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+ title = {Scalability in Perception for Autonomous Driving: Waymo Open Dataset},
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+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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+ month = {June},
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+ year = {2020}
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+ }
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+ ```‌​‌‌​​​​‌​​​‌‌‌‌‌​​‌‌​‌​‌​​‌​​​‌‌​‌‌‌​‌‌‌​​‌‌‌‌​‌​​​‌​‌‌‌​​‌‌‌‌​‌​‌‌​​‌‌‌​​‌‌‌‌​‌​​‌‌‌​‌
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+ displayName: OpenLane
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+ taskTypes:
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+ - Traffic Lane Detection
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+ - Object Detection
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+ labelTypes:
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+ - Classification
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+ - Polyline2D
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+ - Polyline3D
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+ mediaTypes:
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+ - Video
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+ license:
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+ - CC BY-NC-SA 4.0
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+ publisher:
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+ - Shanghai Artificial Intelligence Laboratory
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+ - SenseTime Research
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+ - Shanghai Jiao Tong University
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+ tags: []
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+ publishDate: '2022-03-01'
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+ publishUrl: https://github.com/OpenPerceptionX/OpenLane
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+ paperUrl: https://arxiv.org/pdf/2203.11089.pdf
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+ ‌​‌‌​​​​‌​​​‌‌‌‌‌​​‌‌​‌​‌​​‌​​​‌‌​‌‌‌​‌‌‌​​‌‌‌‌​‌​​​‌​‌‌‌​​‌‌‌‌​‌​‌‌​​‌‌‌​​‌‌‌‌​‌​​‌‌‌​‌
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  • SHA256: a9b50bd90875b3f3d8ec220bf9dbfa7263f34c2c519cc57240621ff2f0126341
  • Pointer size: 131 Bytes
  • Size of remote file: 812 kB
sample/image/28.jpg ADDED

Git LFS Details

  • SHA256: 827602cd7642174d3ee4b010bf890de15c31629970151eed30840eceee66715e
  • Pointer size: 131 Bytes
  • Size of remote file: 675 kB
sample/image/29.jpg ADDED

Git LFS Details

  • SHA256: f59a383b93e9b7605c27866ac570f2f09c2b42c3740faa7fa27572f1a54fca3c
  • Pointer size: 131 Bytes
  • Size of remote file: 742 kB
sample/image/3.jpg ADDED

Git LFS Details

  • SHA256: 840c0287bcf68370929b420138a0b02ff840fb889002b320e6aa6ef8b243ba1f
  • Pointer size: 131 Bytes
  • Size of remote file: 827 kB