Alastair Barber, D. Cosker, Oliver James, Ted Waine, Radhika J. Patel
{"title":"Camera tracking in visual effects an industry perspective of structure from motion","authors":"Alastair Barber, D. Cosker, Oliver James, Ted Waine, Radhika J. Patel","doi":"10.1145/2947688.2947697","DOIUrl":null,"url":null,"abstract":"The 'Matchmove', or camera-tracking process is a crucial task and one of the first to be performed in the visual effects pipeline. An accurate solve for camera movement is imperative and will have an impact on almost every other part of the pipeline downstream. In this work we present a comprehensive analysis of the process at a major visual effects studio, drawing on a large dataset of real shots. We also present guidelines and rules-of-thumb for camera tracking scheduling which are, in what we believe to be an industry first, backed by statistical data drawn from our dataset. We also make available data from our pipeline which shows the amount of time spent on camera tracking and the types of shot that are most common in our work. We hope this will be of interest to the wider computer vision research community and will assist in directing future research.","PeriodicalId":309834,"journal":{"name":"Proceedings of the 2016 Symposium on Digital Production","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 Symposium on Digital Production","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2947688.2947697","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
The 'Matchmove', or camera-tracking process is a crucial task and one of the first to be performed in the visual effects pipeline. An accurate solve for camera movement is imperative and will have an impact on almost every other part of the pipeline downstream. In this work we present a comprehensive analysis of the process at a major visual effects studio, drawing on a large dataset of real shots. We also present guidelines and rules-of-thumb for camera tracking scheduling which are, in what we believe to be an industry first, backed by statistical data drawn from our dataset. We also make available data from our pipeline which shows the amount of time spent on camera tracking and the types of shot that are most common in our work. We hope this will be of interest to the wider computer vision research community and will assist in directing future research.