Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
复制标题
DOI：
--
复制
发表时间：
2018-05
期刊：
影响因子：
--
通讯作者：
Minhyuk Sung;Hao Su;Ronald Yu;L. Guibas
中科院分区：
文献类型：
--
作者：
Minhyuk Sung;Hao Su;Ronald Yu;L. Guibas
各种3D语义属性（例如分割掩模、几何特征、关键点和材料）可以被编码为3D几何形状上的逐点探测函数。给定一组相关的3D形状，我们考虑如何联合分析不同形状的探测函数，以及如何使用神经网络发现共同的潜在结构-即使在没有任何对应信息的情况下。我们的网络是在点云表示的形状几何和相关的语义功能上训练的。这些功能表达了对形状的共享语义理解，但没有以任何方式协调。例如，在分割任务中，函数可以是任意形状部分集合的指示函数，其中所涉及的特定组合对于网络是未知的。我们的网络能够为每个形状生成一个小的基函数字典，这个字典的跨度包括为该形状提供的语义函数。尽管我们的形状具有独立的离散化，并且没有提供功能对应，但网络能够以一致的顺序生成潜在的基础，反映形状之间共享的语义结构。我们证明了我们的技术在各种分割和关键点选择应用的有效性。
Various 3D semantic attributes such as segmentation masks, geometric features, keypoints, and materials can be encoded as per-point probe functions on 3D geometries. Given a collection of related 3D shapes, we consider how to jointly analyze such probe functions over different shapes, and how to discover common latent structures using a neural network --- even in the absence of any correspondence information. Our network is trained on point cloud representations of shape geometry and associated semantic functions on that point cloud. These functions express a shared semantic understanding of the shapes but are not coordinated in any way. For example, in a segmentation task, the functions can be indicator functions of arbitrary sets of shape parts, with the particular combination involved not known to the network. Our network is able to produce a small dictionary of basis functions for each shape, a dictionary whose span includes the semantic functions provided for that shape. Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes. We demonstrate the effectiveness of our technique in various segmentation and keypoint selection applications.