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Signature of position angles histograms for 3D object recognition

机译:用于3D对象识别的位置角直方图的签名

摘要

This paper presents a robust and rotation invariant local surface descriptor by encoding the position angles of neighboring points with a stable and unique local reference frame (LRF) into a 1D histogram. The whole procedure includes two stages: the first stage is to construct a unique LRF by performing eigenvalue decomposition on the covariance matrix formed using all the neighboring points on the local surface. On the second stage, the sphere support field of a key point was divided along the radius into several sphere shells which is similar with the Signature of Histograms OrienTations (SHOT). In each sphere shell, we calculate the cosine of the angles between the neighboring points and the x-axis, z-axis respectively to form two 1D histograms. Finally, all the 1D histograms were stitched together followed by a normalization to generate the local surface descriptor. Experiment results show that our proposed local feature descriptor is robust to noise and varying mesh-resolutions. Moreover, our local feature descriptor based 3D object recognition algorithm achieved a high average recognition rate of 98.9% on the whole UWA dataset.
机译:本文通过将具有稳定且唯一的局部参考系(LRF)的相邻点的位置角编码为一维直方图,提出了一种鲁棒且旋转不变的局部表面描述符。整个过程包括两个阶段:第一个阶段是通过对使用局部表面上所有相邻点形成的协方差矩阵执行特征值分解来构造唯一的LRF。在第二阶段,将关键点的球面支撑场沿半径划分为几个球面壳,这些球面壳与直方图签名特征(SHOT)相似。在每个球壳中,我们分别计算相邻点与x轴,z轴之间的角度的余弦值,以形成两个一维直方图。最后,将所有一维直方图缝合在一起,然后进行归一化以生成局部表面描述符。实验结果表明,我们提出的局部特征描述符对噪声和变化的网格分辨率具有鲁棒性。此外,我们基于局部特征描述符的3D对象识别算法在整个UWA数据集上实现了98.9%的高平均识别率。

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