使用焦点中的光标特征将单个图像配准到3D对象
Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object
摘要：
在2D/3D配准的背景下，本文介绍了一种方法，该方法允许匹配在两种不同的方式检测到的功能：照片和3D模型，通过使用一个共同的2D representation。更准确地说，2D图像与表示3D模型的一组深度图像相匹配。在引入了与曲率估计相关的曲线显著性的概念之后，我们提出了一种新的用于从3D模型绘制的深度图像的脊和谷检测器。该检测器的一种变型适用于照片，特别是通过以多尺度应用该检测器并通过将该特征检测器与聚焦曲线的原理相结合。最后，提出了一种用于确定3D模型的正确视点从而确定姿态的配准算法。它是基于使用直方图的梯度功能，适用于在2D和3D中操作的功能，并引入可重复性分数。所提出的结果突出了检测到的特征的质量，在可重复性方面，以及用于配准和姿态估计的方法的兴趣。
英文摘要：
In the context of 2D/3D registration, this paper introduces an approach that allows to match features detected in two different modalities: photographs and 3D models, by using a common 2D reprensentation. More precisely, 2D images are matched with a set of depth images, representing the 3D model. After introducing the concept of curvilinear saliency, related to curvature estimation, we propose a new ridge and valley detector for depth images rendered from 3D model. A variant of this detector is adapted to photographs, in particular by applying it in multi-scale and by combining this feature detector with the principle of focus curves. Finally, a registration algorithm for determining the correct viewpoint of the 3D model and thus the pose is proposed. It is based on using histogram of gradients features adapted to the features manipulated in 2D and in 3D, and the introduction of repeatability scores. The results presented highlight the quality of the features detected, in term of repeatability, and also the interest of the approach for registration and pose estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
