CHS: Small: Deriving and Exploiting Shape Semantics

3D表示是物理对象的最忠实的数字编码，允许我们存储和操作关于对象的各种信息，包括高级信息（例如，可视性和功能）和低级信息（例如，外观和材料）。此外，它们以一种比2D图像或完全符号表示（如基于文本的知识图谱）更完整的方式进行。然而，将语义信息与3D模型关联起来并不容易，因为直接将3D模型与其功能和使用、语义部分和属性联系起来的数据并不广泛可用。考虑到Web上支持各种应用程序（如3D打印）的3D形状存储库的出现，加上价格合理的3D扫描仪的可用性以及它们在计算机和移动设备中的结合，用语义信息丰富这些存储库的时机已经成熟，这些信息将大大提高它们的可访问性和实用性，而不仅仅是它们最初开发的专门应用程序。PI在这项研究中的目标就是通过开发数学和算法技术来提取、编码和利用3D模型的语义；反过来，这些数据将结合在一个新的3D模型搜索引擎中，该搜索引擎将以统一的方式开发语义形状属性，并允许更广泛的访问和使用3D存储库，从而最终支持许多商业应用，同时也证明对其他研究团体有用。为了实现这些目标，PI将通过几何分析和用户注释的结合来获取关于形状的语义信息。几何分析不仅仅是孤立的形状，而是对相关形状集合的联合分析。其目的是在3D模型之间建立网络，以便在模型之间传递信息。通过这些网络，使用新颖的数学技术，PI将能够理解共享结构以及集合中形状的可变性。形状中的公共部分或结构总是具有语义意义，并且形状在集合中的角色（与其伙伴和对等形状的关系）通常定义形状的语义属性。由于获取用户注释的成本很高，因此计划是开发利用上述网络的工具，以便通过众包查询只需要获得少量注释。然而，用户注释将是稀疏和嘈杂的，因此PI还将开发清理它们以及传播和聚合它们的技术。理解如何将形状几何所反映的语义结构与语言所反映的语义结构相结合是PI在本研究中要解决的深层问题之一。

3D representations are the most faithful digital encoding of physical objects, allowing us to store and manipulate all kinds of information about the object, both high-level (e.g., affordances and functionality) and low-level (e.g., appearance and material). Furthermore, they do so in a way that is more complete than 2D images or entirely symbolic representations such as text-based knowledge graphs. Yet associating semantic information with 3D models is not easy, because data that directly links 3D models to their function and use, their semantic parts and attributes, is not widely available. Given the emergence of 3D shape repositories on the Web supporting a variety of applications (such as 3D printing), plus the availability of affordable 3D scanners and their incorporation in computers and mobile devices, the time is ripe to enrich these repositories with semantic information that will vastly increase their accessibility and usefulness beyond the specialized applications for which they were originally developed. The PI's goal in this research is to do just that, by developing mathematical and algorithmic techniques which extract, encode, and exploit the semantics of 3D models; these in turn will be combined in a novel search engine for 3D models that will exploit semantic shape attributes in a unified way and will allow much broader access to and use of 3D repositories, thereby ultimately supporting many commercial applications while also proving useful to other research communities.Toward these ends, the PI will acquire semantic information about shapes by a combination of geometric analysis and user annotation. The geometric analysis is not only of shapes in isolation, but instead a joint analysis of collections of related shapes. The aim is to build networks among 3D models that can transfer information between them. Through these networks, using novel mathematical techniques, the PI will enable understanding of the shared structure as well as the variability of shapes in a collection. Common parts or structures in shapes invariably have semantic significance, and the role of a shape in a collection (its relationships to its partner and peer shapes) often defines semantic attributes of the shape. Since user annotations are expensive to obtain, the plan is to develop tools that exploit the above-mentioned networks so that only a modest number of annotations need to be obtained by crowd-sourcing queries. However, user annotations will be sparse and noisy, so the PI will also develop techniques for cleaning them up, as well as for propagating and aggregating them. Understanding how to integrate semantic structure reflected in the geometry of shapes with semantic structure reflected in language is one of the deep problems the PI will tackle in this research.