LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2211.05997v1
• Date: Fri, 11 Nov 2022 04:47:33 GMT
• Title: LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
• Authors: Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai
• Abstract summary: LiDAL is a novel active learning method for 3D LiDAR semantic segmentation. We propose LiDAL by exploiting inter-frame uncertainty among LiDAR frames. We achieve 95% of the performance of fully supervised learning with less than 5% of annotations.
• Score: 36.69417080183985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained model should generate robust results irrespective of viewpoints for scene scanning and thus the inconsistencies in model predictions across frames provide a very reliable measure of uncertainty for active sample selection. To implement this uncertainty measure, we introduce new inter-frame divergence and entropy formulations, which serve as the metrics for active selection. Moreover, we demonstrate additional performance gains by predicting and incorporating pseudo-labels, which are also selected using the proposed inter-frame uncertainty measure. Experimental results validate the effectiveness of LiDAL: we achieve 95% of the performance of fully supervised learning with less than 5% of annotations on the SemanticKITTI and nuScenes datasets, outperforming state-of-the-art active learning methods. Code release: https://github.com/hzykent/LiDAL.

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