{"title":"Shape from video","authors":"T. Brodský, C. Fermüller, Y. Aloimonos","doi":"10.1109/CVPR.1999.784622","DOIUrl":null,"url":null,"abstract":"This paper presents a novel technique for recovering the shape of a static scene from a video sequence due to a rigidly moving camera. The solution procedure consists of two stages. In the first stage, the rigid motion of the camera at each instant in time is recovered. This provides the transformation between successive viewing positions. The solution is achieved through new constraints which relate 3D motion and shape directly to the image derivatives. These constraints allow to combine the processes of 3D motion estimation and segmentation by exploiting the geometry and statistics inherent in the data. In the second stage the scene surfaces are reconstructed through an optimization procedure which utilizes data from all the frames of the video sequence. A number of experimental results demonstrate the potential of the approach.","PeriodicalId":20644,"journal":{"name":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1999-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.1999.784622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

Abstract

This paper presents a novel technique for recovering the shape of a static scene from a video sequence due to a rigidly moving camera. The solution procedure consists of two stages. In the first stage, the rigid motion of the camera at each instant in time is recovered. This provides the transformation between successive viewing positions. The solution is achieved through new constraints which relate 3D motion and shape directly to the image derivatives. These constraints allow to combine the processes of 3D motion estimation and segmentation by exploiting the geometry and statistics inherent in the data. In the second stage the scene surfaces are reconstructed through an optimization procedure which utilizes data from all the frames of the video sequence. A number of experimental results demonstrate the potential of the approach.
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来自视频的形状
本文提出了一种从视频序列中恢复静态场景形状的新技术。解决过程包括两个阶段。在第一阶段,恢复相机在每个时刻的刚体运动。这提供了连续观看位置之间的转换。该解决方案是通过将3D运动和形状直接与图像导数相关的新约束来实现的。这些约束允许通过利用数据中固有的几何和统计来结合3D运动估计和分割过程。在第二阶段,通过利用视频序列所有帧的数据的优化程序重构场景表面。大量的实验结果证明了该方法的潜力。
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