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

在LiDAR语义分割领域，本文提出了一种通用而高效的主动学习基准模型Annotator，通过定制化的基于体素的在线选择策略，在每个LiDAR扫描中高效地探测和标注显著和典型的体素格点，即使在分布转移下。Annotator在主动学习、无源域自适应以及域自适应领域表现出色，在LiDAR语义分割基准测试中始终保持出色性能，包括从仿真到真实以及真实到真实的各种情境。令人惊讶的是，Annotator高度高效，只需每个扫描标注五个体素格点，在SynLiDAR-to-SemanticKITTI任务中实现了令人印象深刻的性能，分别在主动学习、无源域自适应以及域自适应下达到了87.8%、88.5%和94.4%。我们预计Annotator将为标签高效的三维应用提供一种简单、通用且高效的解决方案。

注释器：适用于LiDAR语义分割的通用主动学习基线