  Reliance on vast annotations to achieve leading performance severely
restricts the practicality of large-scale point cloud semantic segmentation.
For the purpose of reducing data annotation costs, effective labeling schemes
are developed and contribute to attaining competitive results under weak
supervision strategy. Revisiting current weak label forms, we introduce One
Class One Click (OCOC), a low cost yet informative quasi scene-level label,
which encapsulates point-level and scene-level annotations. An active weakly
supervised framework is proposed to leverage scarce labels by involving weak
supervision from global and local perspectives. Contextual constraints are
imposed by an auxiliary scene classification task, respectively based on global
feature embedding and point-wise prediction aggregation, which restricts the
model prediction merely to OCOC labels. Furthermore, we design a context-aware
pseudo labeling strategy, which effectively supplement point-level supervisory
signals. Finally, an active learning scheme with a uncertainty measure -
temporal output discrepancy is integrated to examine informative samples and
provides guidance on sub-clouds query, which is conducive to quickly attaining
desirable OCOC annotations and reduces the labeling cost to an extremely low
extent. Extensive experimental analysis using three LiDAR benchmarks collected
from airborne, mobile and ground platforms demonstrates that our proposed
method achieves very promising results though subject to scarce labels. It
considerably outperforms genuine scene-level weakly supervised methods by up to
25\% in terms of average F1 score and achieves competitive results against full
supervision schemes. On terrestrial LiDAR dataset - Semantics3D, using
approximately 2\textpertenthousand{} of labels, our method achieves an average
F1 score of 85.2\%, which increases by 11.58\% compared to the baseline model.
