We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable interactions between a shape's point-wise features and those of other shapes. The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation. Our method also proposes a shape retrieval measure to select suitable shapes for cross-shape attention operations for each test shape. Our experiments demonstrate that our approach yields state-of-the-art results in the popular PartNet dataset.

翻译：我们提出了一种深度学习方法来跨形状传播逐点特征表示，旨在实现三维形状分割。我们设计了一种跨形状注意力机制，使一个形状的逐点特征能够与其他形状的特征进行交互。该机制不仅评估点之间的交互程度，还协调跨形状的特征传播，从而提升形状分割任务中逐点特征表示的准确性与一致性。此外，我们的方法提出了一种形状检索度量，用于为每个测试形状选取适合进行跨形状注意力操作的参考形状。实验表明，我们的方法在广泛使用的PartNet数据集上取得了最优结果。