运动注释程序:在大型3D形状集合中注释运动关节的可扩展方法

Xianghao Xu, David Charatan, Sonia Raychaudhuri, Hanxiao Jiang, Mae Heitmann, Vladimir G. Kim, S. Chaudhuri, M. Savva, Angel X. Chang, Daniel Ritchie
{"title":"运动注释程序:在大型3D形状集合中注释运动关节的可扩展方法","authors":"Xianghao Xu, David Charatan, Sonia Raychaudhuri, Hanxiao Jiang, Mae Heitmann, Vladimir G. Kim, S. Chaudhuri, M. Savva, Angel X. Chang, Daniel Ritchie","doi":"10.1109/3DV50981.2020.00071","DOIUrl":null,"url":null,"abstract":"3D models of real-world objects are essential for many applications, including the creation of virtual environments for AI training. To mimic real-world objects in these applications, objects must be annotated with their kinematic mobilities. Annotating kinematic motions is time-consuming, and it is not well-suited to typical crowdsourcing workflows due to the significant domain expertise required. In this paper, we present a system that helps individual expert users rapidly annotate kinematic motions in large 3D shape collections. The organizing concept of our system is motion annotation programs: simple, re-usable procedural rules that generate motion for a given input shape. Our interactive system allows users to author these rules and quickly apply them to collections of functionally-related objects. Using our system, an expert annotated over 1000 joints in under 3 hours. In a user study, participants with no prior experience with our system were able to annotate motions 1.5x faster than with a baseline manual annotation tool.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"240 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Motion Annotation Programs: A Scalable Approach to Annotating Kinematic Articulations in Large 3D Shape Collections\",\"authors\":\"Xianghao Xu, David Charatan, Sonia Raychaudhuri, Hanxiao Jiang, Mae Heitmann, Vladimir G. Kim, S. Chaudhuri, M. Savva, Angel X. Chang, Daniel Ritchie\",\"doi\":\"10.1109/3DV50981.2020.00071\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"3D models of real-world objects are essential for many applications, including the creation of virtual environments for AI training. To mimic real-world objects in these applications, objects must be annotated with their kinematic mobilities. Annotating kinematic motions is time-consuming, and it is not well-suited to typical crowdsourcing workflows due to the significant domain expertise required. In this paper, we present a system that helps individual expert users rapidly annotate kinematic motions in large 3D shape collections. The organizing concept of our system is motion annotation programs: simple, re-usable procedural rules that generate motion for a given input shape. Our interactive system allows users to author these rules and quickly apply them to collections of functionally-related objects. Using our system, an expert annotated over 1000 joints in under 3 hours. In a user study, participants with no prior experience with our system were able to annotate motions 1.5x faster than with a baseline manual annotation tool.\",\"PeriodicalId\":293399,\"journal\":{\"name\":\"2020 International Conference on 3D Vision (3DV)\",\"volume\":\"240 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on 3D Vision (3DV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/3DV50981.2020.00071\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on 3D Vision (3DV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/3DV50981.2020.00071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

现实世界物体的3D模型对于许多应用来说是必不可少的,包括为人工智能训练创建虚拟环境。为了在这些应用程序中模拟现实世界中的对象,必须对对象进行运动学移动注释。注释运动学运动是耗时的,并且由于需要大量的领域专业知识,它不适合典型的众包工作流程。在本文中,我们提出了一个系统,可以帮助个人专家用户快速注释大型3D形状集合中的运动学运动。我们系统的组织概念是运动注释程序:简单,可重用的过程规则,为给定的输入形状生成运动。我们的交互式系统允许用户编写这些规则,并快速将它们应用于与功能相关的对象集合。使用我们的系统,专家在3小时内注释了1000多个关节。在一项用户研究中,没有使用我们系统经验的参与者能够比使用基线手动注释工具快1.5倍地注释动作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion Annotation Programs: A Scalable Approach to Annotating Kinematic Articulations in Large 3D Shape Collections
3D models of real-world objects are essential for many applications, including the creation of virtual environments for AI training. To mimic real-world objects in these applications, objects must be annotated with their kinematic mobilities. Annotating kinematic motions is time-consuming, and it is not well-suited to typical crowdsourcing workflows due to the significant domain expertise required. In this paper, we present a system that helps individual expert users rapidly annotate kinematic motions in large 3D shape collections. The organizing concept of our system is motion annotation programs: simple, re-usable procedural rules that generate motion for a given input shape. Our interactive system allows users to author these rules and quickly apply them to collections of functionally-related objects. Using our system, an expert annotated over 1000 joints in under 3 hours. In a user study, participants with no prior experience with our system were able to annotate motions 1.5x faster than with a baseline manual annotation tool.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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