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}
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.