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重新研究基于图像的本地化的图像检索

Image Retrieval for Image-Based Localization Revisited
课程网址: http://videolectures.net/bmvc2012_sattler_image_retrieval/  
主讲教师: Torsten Sattler
开课单位: 亚琛工业大学
开课时间: 2012-10-09
课程语种: 英语
中文简介:
为了可靠地确定图像相对于3D点云的相机姿态 场景,需要2D特征和3D点之间的对应关系。最近的工作有 证明直接匹配要点的特征优于方法 根据可以的图像数量进行中间图像检索步骤 成功本地化。然而,直接匹配本质上不如基于检索的可扩展性 方法。因此,在本文中,我们分析了导致的算法因素 绩效差距和认定误判票是差距的主要来源。基于 在详细的实验评估中,我们展示了使用选择性的检索方法 投票方案能够胜过最先进的直接匹配方法。我们探索 如何加速选择性投票和通信计算 使用汉明嵌入特征描述符。此外,我们介绍一个新的 具有挑战性查询图像的数据集,用于评估基于图像的定位。
课程简介: To reliably determine the camera pose of an image relative to a 3D point cloud of a scene, correspondences between 2D features and 3D points are needed. Recent work has demonstrated that directly matching the features against the points outperforms methods that take an intermediate image retrieval step in terms of the number of images that can be localized successfully. Yet, direct matching is inherently less scalable than retrievalbased approaches. In this paper, we therefore analyze the algorithmic factors that cause the performance gap and identify false positive votes as the main source of the gap. Based on a detailed experimental evaluation, we show that retrieval methods using a selective voting scheme are able to outperform state-of-the-art direct matching methods. We explore how both selective voting and correspondence computation can be accelerated by using a Hamming embedding of feature descriptors. Furthermore, we introduce a new dataset with challenging query images for the evaluation of image-based localization.
关 键 词: 3D点云; 汉明嵌入特征描述符; 通信计算
课程来源: 视频讲座网
最后编审: 2020-06-06:liush
阅读次数: 46