We present ConceptFactory, a novel scope to facilitate more efficient annotation of 3D object knowledge by recognizing 3D objects through generalized concepts (i.e. object conceptualization), aiming at promoting machine intelligence to learn comprehensive object knowledge from both vision and robotics aspects. This idea originates from the findings in human cognition research that the perceptual recognition of objects can be explained as a process of arranging generalized geometric components (e.g. cuboids and cylinders). ConceptFactory consists of two critical parts: i) ConceptFactory Suite, a unified toolbox that adopts Standard Concept Template Library (STL-C) to drive a web-based platform for object conceptualization, and ii) ConceptFactory Asset, a large collection of conceptualized objects acquired using ConceptFactory suite. Our approach enables researchers to effortlessly acquire or customize extensive varieties of object knowledge to comprehensively study different object understanding tasks. We validate our idea on a wide range of benchmark tasks from both vision and robotics aspects with state-of-the-art algorithms, demonstrating the high quality and versatility of annotations provided by our approach. Our website is available at https://apeirony.github.io/ConceptFactory.

翻译：我们提出了ConceptFactory，一种新颖的框架，旨在通过广义概念（即对象概念化）识别三维物体，从而促进更高效的三维物体知识标注，其目标是推动机器智能从视觉与机器人学两方面学习全面的物体知识。这一想法源于人类认知研究的发现，即物体的感知识别可解释为排列广义几何组件（如立方体和圆柱体）的过程。ConceptFactory包含两个关键部分：i) ConceptFactory套件，一个采用标准概念模板库（STL-C）驱动的、基于Web的对象概念化统一工具箱；ii) ConceptFactory资产，一个使用ConceptFactory套件获取的大规模概念化物体集合。我们的方法使研究人员能够轻松获取或定制广泛多样的物体知识，以全面研究不同的物体理解任务。我们在视觉与机器人学领域的多种基准任务上，使用最先进的算法验证了我们的理念，证明了本方法所提供标注的高质量与多功能性。我们的网站地址为：https://apeirony.github.io/ConceptFactory。