  The expensive annotation cost is notoriously known as the main constraint for
the development of the point cloud semantic segmentation technique. Active
learning methods endeavor to reduce such cost by selecting and labeling only a
subset of the point clouds, yet previous attempts ignore the spatial-structural
diversity of the selected samples, inducing the model to select clustered
candidates with similar shapes in a local area while missing other
representative ones in the global environment. In this paper, we propose a new
3D region-based active learning method to tackle this problem. Dubbed SSDR-AL,
our method groups the original point clouds into superpoints and incrementally
selects the most informative and representative ones for label acquisition. We
achieve the selection mechanism via a graph reasoning network that considers
both the spatial and structural diversities of superpoints. To deploy SSDR-AL
in a more practical scenario, we design a noise-aware iterative labeling
strategy to confront the "noisy annotation" problem introduced by the previous
"dominant labeling" strategy in superpoints. Extensive experiments on two point
cloud benchmarks demonstrate the effectiveness of SSDR-AL in the semantic
segmentation task. Particularly, SSDR-AL significantly outperforms the baseline
method and reduces the annotation cost by up to 63.0% and 24.0% when achieving
90% performance of fully supervised learning, respectively.
