SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds

Understand

Labelling point clouds fully is highly time-consuming and costly.

• As larger point cloud datasets with billions of points become more common, we ask whether the full annotation is even necessary, demonstrating that existing baselines designed under a fully annotated assumption only degrade slightly even when faced with 1% random point annotations.
• However, beyond this point, e.g., at 0.1% annotations, segmentation accuracy is unacceptably low.
• We observe that, as point clouds are samples of the 3D world, the distribution of points in a local neighborhood is relatively homogeneous, exhibiting strong semantic similarity.