  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
