Abstract A common approach to recognition of objects in cluttered scenes is to generate hypotheses about objects present in the scene by matching local descriptors of point features. These hypotheses are then evaluated by measuring how well they explain a particular part of the scene. In this paper, we investigate an alternative approach, which is based on alignment of convex hulls of segments detected in a depth image with convex hulls of target 3D object models or their parts. This alignment is performed using the Convex Template Instance descriptor. This descriptor was originally proposed for fruit recognition and classification of segmented objects. We have adapted this approach to recognize objects in complex scenes. Furthermore, we propose a novel three-level hypothesis evaluation strategy which can be used to achieve highly efficient object recognition. The proposed approach is evaluated by comparison with nine state-of-the-art approaches using three challenging benchmark datasets.

中文翻译：

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基于凸包对齐的物体识别

摘要 在杂乱场景中识别对象的常用方法是通过匹配点特征的局部描述符来生成关于场景中存在的对象的假设。然后通过测量它们对场景特定部分的解释程度来评估这些假设。在本文中，我们研究了一种替代方法，该方法基于在深度图像中检测到的段的凸包与目标 3D 对象模型或其部分的凸包的对齐。这种对齐是使用凸模板实例描述符执行的。该描述符最初被提出用于水果识别和分割对象的分类。我们已经采用这种方法来识别复杂场景中的对象。此外，我们提出了一种新颖的三级假设评估策略，可用于实现高效的对象识别。通过与使用三个具有挑战性的基准数据集的九种最先进的方法进行比较来评估所提出的方法。