We present a method that processes 3D point clouds by performing graph convolution operations across shapes. In this manner, point descriptors are learned by allowing interaction and propagation of feature representations within a shape collection. To enable this form of non-local, cross-shape graph convolution, our method learns a pairwise point attention mechanism indicating the degree of interaction between points on different shapes. Our method also learns to create a graph over shapes of an input collection whose edges connect shapes deemed as useful for performing cross-shape convolution. The edges are also equipped with learned weights indicating the compatibility of each shape pair for cross-shape convolution. Our experiments demonstrate that this interaction and propagation of point representations across shapes make them more discriminative. In particular, our results show significantly improved performance for 3D point cloud semantic segmentation compared to conventional approaches, especially in cases with the limited number of training examples.

中文翻译：

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交叉形状图卷积网络

我们提出了一种通过跨形状执行图形卷积操作来处理 3D 点云的方法。以这种方式，通过允许形状集合内特征表示的交互和传播来学习点描述符。为了实现这种形式的非局部交叉形状图卷积，我们的方法学习了成对点注意机制，指示不同形状上的点之间的交互程度。我们的方法还学习在输入集合的形状上创建图，其边连接被认为对执行交叉形状卷积有用的形状。边缘还配备了学习权重，表明每个形状对对于交叉形状卷积的兼容性。我们的实验表明，这种跨形状的点表示的交互和传播使它们更具辨别力。特别是，我们的结果表明，与传统方法相比，3D 点云语义分割的性能显着提高，尤其是在训练示例数量有限的情况下。