Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering annotation labor-intensive and cost-prohibitive. This paper presents Annotator, a general and efficient active learning baseline, in which a voxel-centric online selection strategy is tailored to efficiently probe and annotate the salient and exemplar voxel girds within each LiDAR scan, even under distribution shift. Concretely, we first execute an in-depth analysis of several common selection strategies such as Random, Entropy, Margin, and then develop voxel confusion degree (VCD) to exploit the local topology relations and structures of point clouds. Annotator excels in diverse settings, with a particular focus on active learning (AL), active source-free domain adaptation (ASFDA), and active domain adaptation (ADA). It consistently delivers exceptional performance across LiDAR semantic segmentation benchmarks, spanning both simulation-to-real and real-to-real scenarios. Surprisingly, Annotator exhibits remarkable efficiency, requiring significantly fewer annotations, e.g., just labeling five voxels per scan in the SynLiDAR-to-SemanticKITTI task. This results in impressive performance, achieving 87.8% fully-supervised performance under AL, 88.5% under ASFDA, and 94.4% under ADA. We envision that Annotator will offer a simple, general, and efficient solution for label-efficient 3D applications. Project page: https://binhuixie.github.io/annotator-web

翻译：主动学习作为一种标签高效范式，使模型能够主动与标注者交互以获取新数据标注。在激光雷达语义分割领域，点云数据规模的庞大性使得标注工作人力密集且成本高昂。本文提出注释器——一个通用高效的主动学习基线方法，其核心创新在于提出体素中心在线选择策略，可在分布偏移条件下高效探测和标注每个激光雷达扫描中的显著及典型体素网格。具体而言，我们首先深入分析了随机、熵、边际等多种常见选择策略，随后开发体素混淆度（VCD）以挖掘点云的局部拓扑关系与结构特性。注释器在多种场景中表现卓越，特别聚焦于主动学习（AL）、主动无源领域自适应（ASFDA）和主动域自适应（ADA）三大任务。该方法在跨模拟到真实、真实到真实场景的激光雷达语义分割基准测试中始终展现优异性能。令人瞩目的是，注释器展现出显著效率优势，例如在SynLiDAR到SemanticKITTI任务中，每帧仅需标注五个体素即可实现：在主动学习场景下达到全监督性能的87.8%，在ASFDA中达88.5%，在ADA中达94.4%。我们期望注释器能为标签高效的三维应用提供简单通用高效的解决方案。项目页面：https://binhuixie.github.io/annotator-web