论文标题
学习隐式功能，用于拓扑变密的3D形状对应关系
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
论文作者
论文摘要
本文的目的是以无监督的方式学习与拓扑变化对象的致密3D形状对应。常规隐式函数估计给定形状潜在代码的3D点的占用。取而代之的是，我们的新颖隐式函数为每个3D点产生一个嵌入向量的零件嵌入向量，该函数被认为与同一对象类别的另一个3D形状中的密集对应点相似。此外，我们通过从嵌入到相应的3D点的零件映射的逆函数映射来实现密集的对应关系。这两个功能都具有多个有效的损失功能共同学习，以实现我们的假设，并生成形状潜在代码的编码器。在推断期间，如果用户在源形状上选择一个任意点，我们的算法可以自动生成一个置信分数，指示目标形状上是否存在对应关系，以及是否有一个对应的语义点。这种机制固有地使人造物体具有不同的构成。通过无监督的3D语义对应关系和形状分割证明了我们方法的有效性。
The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a part embedding vector for each 3D point, which is assumed to be similar to its densely corresponded point in another 3D shape of the same object category. Furthermore, we implement dense correspondence through an inverse function mapping from the part embedding to a corresponded 3D point. Both functions are jointly learned with several effective loss functions to realize our assumption, together with the encoder generating the shape latent code. During inference, if a user selects an arbitrary point on the source shape, our algorithm can automatically generate a confidence score indicating whether there is a correspondence on the target shape, as well as the corresponding semantic point if there is one. Such a mechanism inherently benefits man-made objects with different part constitutions. The effectiveness of our approach is demonstrated through unsupervised 3D semantic correspondence and shape segmentation.
