Point cloud semantic segmentation based on deep learning methods is still a challenge due to the irregularity of structures and uncertainty of sampling. Color information often contains a lot of prior information, whereas the existing methods do not attach more importance to it. To deal with this problem, we propose a novel hard attention mechanism, named color-guided convolution. This convolution operator learns the correlation between geometric and color information by reordering the local points with color-indicated vectors. In addition, the global feature fusion is proposed to rectify features selected by the feature selecting unit. Experimental results and comparisons with recent methods demonstrate the superiority of our approach.

中文翻译：

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用于点云语义分割的颜色引导卷积网络

由于结构的不规则性和采样的不确定性，基于深度学习方法的点云语义分割仍然是一个挑战。颜色信息往往包含大量的先验信息，而现有的方法对其并不重视。为了解决这个问题，我们提出了一种新的硬注意力机制，称为颜色引导卷积。这个卷积算子通过用颜色表示的向量对局部点重新排序来学习几何和颜色信息之间的相关性。此外，提出了全局特征融合来纠正特征选择单元选择的特征。实验结果和与最近方法的比较证明了我们方法的优越性。