论文标题
3D点云部分分割的跨形关注
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
论文作者
论文摘要
我们提出了一种深度学习方法，该方法为3D形状分割的目的传播了集合中跨形状的点特征表示。我们提出了一种跨形注意机制，以实现形状的点特征与其他形状的相互作用。该机制评估了点之间的相互作用程度，也评估了跨形状的介导特征传播，从而提高了形状分割所得的点特征表示的准确性和一致性。我们的方法还提出了一种形状检索度量，以选择适合每个测试形状的跨形注意操作的形状。我们的实验表明，我们的方法产生了最新的partnet数据集。
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.
