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HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling

2023-01-25 08:40:34
Fenggen Yu, Yiming Qian, Francisca Gil-Ureta, Brian Jackson, Eric Bennett, Hao Zhang

Abstract

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.

Abstract (translated)

我们提出了第一个精细粒度的三维部件标记工具,这个问题甚至挑战了最先进的深度学习方法,因为微小的精细部件之间存在巨大的结构差异。由于这个原因,必要的数据标注工作量非常大,激励方法尽量减少人类参与。我们的标记工具迭代地验证或修改由深度神经网络预测的部件标签,通过人类反馈不断改善网络预测。为了有效地减少人类工作量,我们开发了两项新特征,包括层次结构和对称性aware的 Active Labeling。我们的人类参与循环方法称为HAL3D,能够在具有预先定义层次部件标签的任何测试集上实现100%准确性(排除人类错误),比手动工作节省80%的时间。

URL

https://arxiv.org/abs/2301.10460

PDF

https://arxiv.org/pdf/2301.10460.pdf


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