论文标题
无监督的内在结构表示点
Unsupervised Learning of Intrinsic Structural Representation Points
论文作者
论文摘要
3D形状的学习结构是计算机图形和几何处理领域的基本问题。我们提出了一种简单但可解释的无监督方法，用于以3D结构点的形式学习新的结构表示。我们方法产生的3D结构点本质上编码了形状结构，并在所有形状实例上都具有相似结构的语义一致性。这是一个具有挑战性的目标，尚未通过其他方法完全实现。具体来说，我们的方法将3D点云作为输入，并将其编码为一组本地功能。然后，局部特征通过一个新的点积分模块，以产生一组3D结构点。倒角距离用作重建损失，以确保结构点位于输入点云附近。广泛的实验表明，我们的方法在语义形状对应任务上的最新方法优于最先进，并且与细分标签传输任务上的最新表现相当的性能。此外，基于PCA的形状嵌入基于一致的结构点，在保留形状结构方面表现出良好的性能。代码可在https://github.com/nolenchen/3dstructurepoint上获得
Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure points. The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures. This is a challenging goal that has not fully been achieved by other methods. Specifically, our method takes a 3D point cloud as input and encodes it as a set of local features. The local features are then passed through a novel point integration module to produce a set of 3D structure points. The chamfer distance is used as reconstruction loss to ensure the structure points lie close to the input point cloud. Extensive experiments have shown that our method outperforms the state-of-the-art on the semantic shape correspondence task and achieves comparable performance with the state-of-the-art on the segmentation label transfer task. Moreover, the PCA based shape embedding built upon consistent structure points demonstrates good performance in preserving the shape structures. Code is available at https://github.com/NolenChen/3DStructurePoints
