ConceptFactory: Facilitate 3D Object Knowledge Annotation with Object Conceptualization

TL;DR

ConceptFactory introduces a new framework for annotating 3D objects using generalized concepts, inspired by human cognition, to enhance machine learning in vision and robotics.

Contribution

It provides a unified toolbox and a large collection of conceptualized objects to improve 3D object annotation and understanding tasks.

Findings

Validated on diverse benchmark tasks with state-of-the-art algorithms.

Demonstrated high quality and versatile annotations for 3D objects.

Facilitated extensive object knowledge acquisition for research.

Abstract

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.