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HUSC: A local feature descriptor of point cloud based on hemisphere neighborhood

机译：HESC：基于半球邻域的点云的本地特征描述符

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摘要

In order to improve the efficiency of LiDAR point cloud object recognition and reduce the computational overhead, a new feature descriptor, Hemispheric Unique Shape Context (HUSC), is presented in this paper by using an improved neighborhood determination method. Firstly, the normal vector and tangent plane at key point are estimated and the local reference frame is established. Then a hemispherical neighborhood is constructed based on the tangent plane and divided into bins according to azimuth, polar angle and radial direction. Finally, the points in each bin are counted and the local feature descriptors of key points are obtained. HUSC feature descriptor can not only ensure the discriminability of descriptors, but also improve the efficiency of object recognition by reducing the number of free bins. Experiments on Bologna dataset and 3DMatch dataset show that HUSC feature descriptor with hemispheric neighborhood is robust to noise, occupying less memory and operating faster.
机译：为了提高LIDAR点云对象识别的效率并降低计算开销，通过使用改进的邻域确定方法，本文提出了一种新的特征描述符，半球独特形状上下文（HESC）。 首先，估计键点处的正常矢量和切线平面，并建立局部参考帧。 然后基于切线平面构造半球形邻域，并根据方位角，极性角度和径向分成箱。 最后，计算每个箱中的点，并且获得关键点的局部特征描述符。 HESC特征描述符不仅可以确保描述符的可辨认性，而且还通过减少自由箱的数量来提高物体识别的效率。 博洛尼亚数据集和3DMATCH数据集的实验显示，带半球邻域的HUSC功能描述符是强大的噪声，占用更少的内存并更快工作。

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