论文标题
LIDAL：3D激光雷达语义分割的基于框架间的不确定性主动学习
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
论文作者
论文摘要
我们提出了LIDAL，这是一种通过利用LIDAR框架之间的框架间的不确定性来实现3D激光雷达语义分割的新型活性学习方法。我们的核心思想是，训练有素的模型应产生强大的结果，而与场景扫描的观点无关，因此，跨帧的模型预测中的不一致性为主动样品选择的不确定性提供了非常可靠的度量。为了实施这种不确定性度量，我们引入了新的框架间差异和熵配方，这些配方是积极选择的指标。此外，我们通过预测和合并伪标签来证明额外的性能增长，这些标签也可以使用拟议的框架间的不确定性度量选择。实验结果验证了赖以生存的有效性：我们在Semantickitti和Nuscenes数据集中获得了不到5％的注释，达到了完全监督学习的95％，表现优于先进的主动学习方法。代码发布：https：//github.com/hzykent/lidal。
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.
