From structure-from-motion point clouds to fast location recognition
From structure-from-motion point clouds to fast location recognition
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DOI：
10.1109/cvpr.2009.5206587
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发表时间：
2009-06
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影响因子：
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通讯作者：
Arnold Irschara;C. Zach;Jan-Michael Frahm;H. Bischof
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文献类型：
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作者：
Arnold Irschara;C. Zach;Jan-Michael Frahm;H. Bischof
针对给定3D重建的高效视图配准有许多应用，如室内和室外环境中的内外跟踪，以及大型照相集中的地理定位图像。提出了一种基于运动点云结构的快速位置识别技术。基于词汇树的特征索引直接返回3D模型的相关片段，而不是图像数据库中的文档。此外，我们提出了一种压缩的3D场景表示，提高了识别率，同时减少了计算时间和内存消耗。我们的方法的设计是基于算法，有效地利用现代图形处理单元，以提供实时性能的视图注册。我们通过将手持户外视频与已知的3D城市模型相匹配，并通过将在线图片集中的图像注册到相应的地标来演示该方法。
Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.