Openable part detection is the task of detecting the openable parts of an object in a single-view image, and predicting corresponding motion parameters. Prior work investigated the unrealistic setting where all input images only contain a single openable object. We generalize this task to scenes with multiple objects each potentially possessing openable parts, and create a corresponding dataset based on real-world scenes. We then address this more challenging scenario with OPDFormer: a part-aware transformer architecture. Our experiments show that the OPDFormer architecture significantly outperforms prior work. The more realistic multiple-object scenarios we investigated remain challenging for all methods, indicating opportunities for future work.
@article{arxiv.2303.14087,
title = {OPDMulti: Openable Part Detection for Multiple Objects},
author = {Xiaohao Sun and Hanxiao Jiang and Manolis Savva and Angel Xuan Chang},
journal= {arXiv preprint arXiv:2303.14087},
year = {2023}
}