A Performance Evaluation of Volumetric 3D Interest Point Detectors
A Performance Evaluation of Volumetric 3D Interest Point Detectors
复制标题
DOI：
10.1007/s11263-012-0563-2
复制
发表时间：
2013-03-01
影响因子：
19.5
通讯作者：
Cipolla, Roberto
中科院分区：
文献类型：
--
作者：
Yu, Tsz-Ho;Woodford, Oliver J.;Cipolla, Roberto
本文首次提出了兴趣点在标量体积数据上的性能评价。这些数据编码三维形状，这是物体的基本属性。使用另一种这样的属性，纹理（即二维表面着色）或外观进行对象检测、识别和注册已经得到了很好的研究；3D形状则不然。然而，3D形状获取技术的日益普及和从外观上获得的收益递减已经看到了基于3D形状的方法的激增。在这项工作中，我们研究了几种最先进的兴趣点检测器在体积数据中的性能，包括可重复性、兴趣点的数量和性质。这些方法构成了许多基于形状的应用程序的第一步。我们详细比较了合成和真实三维数据的定量和定性测量，包括基于点和体积的测量，帮助读者选择适合他们应用的方法。
This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of 3D shape acquisition techniques and the diminishing returns to be had from appearance alone have seen a surge in 3D shape-based methods. In this work, we investigate the performance of several state of the art interest points detectors in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application.