In the last decades, 3-D object recognition has received significant attention. Particularly, in the presence of clutter and occlusion, 3-D object recognition is a challenging task. In this article, we present an object recognition pipeline to identify the objects from cluttered scenes. A highly descriptive, robust, and computationally efficient local shape descriptor (LSD) is first designed to establish the correspondences between a model point cloud and a scene point cloud. Then, a clustering method, which utilizes the local reference frames (LRFs) of the keypoints, is proposed to select the correct correspondences. Finally, an index is developed to verify the transformation hypotheses. The experiments are conducted to validate the proposed object recognition method. The experimental results demonstrate that the proposed LSD holds high descriptor matching performance and the clustering method can well group the correct correspondences. The index is also very effective to filter the false transformation hypotheses. All these enhance the recognition performance of our method.

中文翻译：

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杂乱场景中基于局部形状描述的3-D目标识别流水线

在过去的几十年中，3-D对象识别得到了极大的关注。特别是在杂波和遮挡的情况下，3D对象识别是一项艰巨的任务。在本文中，我们提出了一个对象识别管道，以从混乱的场景中识别出对象。首先，设计一种具有高度描述性，鲁棒性和计算效率的局部形状描述符（LSD），以建立模型点云和场景点云之间的对应关系。然后，提出了一种利用关键点的本地参考帧（LRF）的聚类方法来选择正确的对应关系。最后，开发一个索引来验证变换假设。进行实验以验证提出的目标识别方法。实验结果表明，所提出的LSD具有较高的描述符匹配性能，聚类方法可以很好地对正确的对应关系进行分组。该索引对于过滤错误的转换假设也非常有效。所有这些增强了我们方法的识别性能。