PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds

Yucheng Zhang, Masaki Fukuda, Yasunori Ishii, Kyoko Ohshima, Takayoshi Yamashita
{"title":"PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds","authors":"Yucheng Zhang, Masaki Fukuda, Yasunori Ishii, Kyoko Ohshima, Takayoshi Yamashita","doi":"10.23919/MVA57639.2023.10216156","DOIUrl":null,"url":null,"abstract":"3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to deep learning. However, deep learning algorithms are data-driven and require large amounts of annotated point cloud data for training and evaluation. Unlike 2D images, annotating point cloud data is difficult due to the limitations of sparsity, irregularity, and low resolution, which requires more manual work, and the annotation efficiency is much lower than annotating 2D images. Therefore, we propose an annotation algorithm for point cloud data, which is pre-annotation and camera-LiDAR late fusion algorithm to annotate easily and accurately.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 18th International Conference on Machine Vision and Applications (MVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/MVA57639.2023.10216156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to deep learning. However, deep learning algorithms are data-driven and require large amounts of annotated point cloud data for training and evaluation. Unlike 2D images, annotating point cloud data is difficult due to the limitations of sparsity, irregularity, and low resolution, which requires more manual work, and the annotation efficiency is much lower than annotating 2D images. Therefore, we propose an annotation algorithm for point cloud data, which is pre-annotation and camera-LiDAR late fusion algorithm to annotate easily and accurately.
点云的预标注和相机-激光雷达后期融合
在自动驾驶领域,三维目标检测已成为不可或缺的技术。迄今为止,在深度学习的推动下,三维物体检测研究取得了可喜的突破。然而,深度学习算法是数据驱动的,需要大量带注释的点云数据进行训练和评估。与二维图像不同,点云数据的标注由于其稀疏性、不规则性和低分辨率的限制,标注难度较大,需要更多的手工操作,且标注效率远低于二维图像。为此,我们提出了一种点云数据标注算法,即预标注和相机-激光雷达后期融合算法,以方便、准确地标注点云数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信