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Learning to Detect Good 3D Keypoints
International Journal of Computer Vision ( IF 19.5 ) Pub Date : 2017-08-08 , DOI: 10.1007/s11263-017-1037-3
Alessio Tonioni , Samuele Salti , Federico Tombari , Riccardo Spezialetti , Luigi Di Stefano

The established approach to 3D keypoint detection consists in defining effective handcrafted saliency functions based on geometric cues with the aim of maximizing keypoint repeatability. Differently, the idea behind our work is to learn a descriptor-specific keypoint detector so as to optimize the end-to-end performance of the feature matching pipeline. Accordingly, we cast 3D keypoint detection as a classification problem between surface patches that can or cannot be matched correctly by a given 3D descriptor, i.e. those either good or not in respect to that descriptor. We propose a machine learning framework that allows for defining examples of good surface patches from the training data and leverages Random Forest classifiers to realize both fixed-scale and adaptive-scale 3D keypoint detectors. Through extensive experiments on standard datasets, we show how feature matching performance improves significantly by deploying 3D descriptors together with companion detectors learned by our methodology with respect to the adoption of established state-of-the-art 3D detectors based on hand-crafted saliency functions.

中文翻译:

学习检测好的 3D 关键点

已建立的 3D 关键点检测方法包括基于几何线索定义有效的手工显着性函数,目的是最大化关键点可重复性。不同的是,我们工作背后的想法是学习特定于描述符的关键点检测器,以优化特征匹配管道的端到端性能。因此,我们将 3D 关键点检测作为表面补丁之间的分类问题,这些表面补丁可以或不能被给定的 3D 描述符正确匹配,即那些关于该描述符的好或不好。我们提出了一个机器学习框架,它允许从训练数据中定义好的表面补丁的例子,并利用随机森林分类器来实现固定尺度和自适应尺度 3D 关键点检测器。通过对标准数据集的大量实验,
更新日期:2017-08-08
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