Human-designed visual manuals are crucial components in shape assembly activities. They provide step-by-step guidance on how we should move and connect different parts in a convenient and physically-realizable way. While there has been an ongoing effort in building agents that perform assembly tasks, the information in human-design manuals has been largely overlooked. We identify that this is due to 1) a lack of realistic 3D assembly objects that have paired manuals and 2) the difficulty of extracting structured information from purely image-based manuals. Motivated by this observation, we present IKEA-Manual, a dataset consisting of 102 IKEA objects paired with assembly manuals. We provide fine-grained annotations on the IKEA objects and assembly manuals, including decomposed assembly parts, assembly plans, manual segmentation, and 2D-3D correspondence between 3D parts and visual manuals. We illustrate the broad application of our dataset on four tasks related to shape assembly: assembly plan generation, part segmentation, pose estimation, and 3D part assembly.
@article{arxiv.2302.01881,
title = {IKEA-Manual: Seeing Shape Assembly Step by Step},
author = {Ruocheng Wang and Yunzhi Zhang and Jiayuan Mao and Ran Zhang and Chin-Yi Cheng and Jiajun Wu},
journal= {arXiv preprint arXiv:2302.01881},
year = {2023}
}
Comments
NeurIPS 2022 Datasets and Benchmarks Track. Project page: https://cs.stanford.edu/~rcwang/projects/ikea_manual