Collaborative Research: CI-P: ShapeNet: An Information-Rich 3D Model Repository for Graphics, Vision and Robotics Research

该项目的目标是计划开发一个名为ShapeNet的具有丰富注释的3D模型库，该库目前仅以初步形式存在。ShapeNet将包含300万至400万个日常物品的3D模型，分为4千至5千个类别，采用各种表示形式。ShapeNet存储库中的模型将使用多种注释类型进行注释：几何（部件，对称性），语义（形状及其部件的关键字），物理（重量，大小）和功能（启示，场景上下文）。ShapeNet数据的可用性，捕捉了世界上很大一部分物体类别的3D几何形状，以及相关的详细元数据和语义信息，将促进图形学的重大发展，视觉和机器人技术，通过提供足够的数据，对新提出的技术和方法的形状或场景分析和合成可以审查-并且可以用其训练机器学习算法。ShapeNet可以被看作是一本百科全书，它有助于创建能够在世界上自主运行的智能系统和智能体，因为它们可以对世界有深入的了解。虽然大多数ShapeNet模型最初将在Web上找到，但注释将通过适度的人工输入的主动学习组合来获得（包括众包），广泛的算法传输和人工验证。在规划期间，工作重点将是与3D模型相关的语义知识的数学表示，以及允许知识从一个模型传输到另一个模型的关键算法的设计框架。需要解决的进一步挑战包括数据质量问题的量化以及所有多模式（3D，图像，语言）UI和API的规范，这些UI和API需要用户能够利用和搜索这些丰富的数据，或者为其提供额外的模型和注释。

The goal of this project is to plan the development of a richly annotated repository of 3D models called ShapeNet that currently exists only in a preliminary form. ShapeNet will include 3-4 million 3D models of everyday objects in 4-5 thousand categories, in a variety of representations. Models in the ShapeNet repository will be annotated with multiple annotation types: geometric (parts, symmetries), semantic (keywords for the shape and its parts), physical (weight, size), and functional (affordances, scene context). The availability of ShapeNet data, capturing the 3D geometry of a significant fraction of object categories in the world, together with associated detailed meta-data and semantic information, will catalyze major developments in graphics, vision and robotics by providing adequate data against which new proposed techniques and methodologies for shape or scene analysis and synthesis can be vetted -- and with which machine learning algorithms can be trained. ShapeNet can be considered an encyclopedia that facilitates the creation of intelligent systems and agents capable of operating autonomously in the world --- because they can have deep knowledge of that world.While most of the ShapeNet models will be initially found on the Web, the annotations will be obtained through an active learning combination of modest human input (including crowd-sourcing), extensive algorithmic transport, and human verification. During the planning period the effort will focus on mathematical representations of the semantic knowledge associated with 3D models, as well as on a design framework for key algorithms allowing knowledge transport from one model to another. Further challenges to be addressed include the quantification of data quality issues and the specification of all the multimodal (3D, image, language) UIs and APIs needed for users to be able to exploit and search this wealth of data, or to contribute additional models and annotations to it.