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

TL;DR

HAL3D introduces a hierarchical, symmetry-aware active learning tool that significantly reduces human effort while achieving perfect accuracy in fine-grained 3D part labeling.

Contribution

The paper presents the first active learning framework for fine-grained 3D part labeling, incorporating hierarchical and symmetry-aware features to improve efficiency.

Findings

Achieves 100% accuracy on test sets with pre-defined labels.

Reduces human annotation effort by 80%.

First to apply active learning to fine-grained 3D part labeling.

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.