We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts. For the same reason, the necessary data annotation effort is tremendous, motivating approaches to minimize human involvement. Our labeling tool iteratively verifies or modifies part labels predicted by a deep neural network, with human feedback continually improving the network prediction. To effectively reduce human efforts, we develop two novel features in our tool, hierarchical and symmetry-aware active labeling. Our human-in-the-loop approach, coined HAL3D, achieves 100% accuracy (barring human errors) on any test set with pre-defined hierarchical part labels, with 80% time-saving over manual effort.
@article{arxiv.2301.10460,
title = {HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling},
author = {Fenggen Yu and Yiming Qian and Francisca Gil-Ureta and Brian Jackson and Eric Bennett and Hao Zhang},
journal= {arXiv preprint arXiv:2301.10460},
year = {2024}
}