SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
This reduces annotation effort for 3D point cloud segmentation, though it is incremental as it builds on existing weak supervision ideas.
The paper tackles the problem of high annotation cost for large-scale 3D point cloud segmentation by proposing a weakly-supervised method that uses only 0.1% randomly annotated points for training, achieving promising performance on seven datasets.
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. Motivated by this, we propose a new weak supervision method to implicitly augment highly sparse supervision signals. Extensive experiments demonstrate the proposed Semantic Query Network (SQN) achieves promising performance on seven large-scale open datasets under weak supervision schemes, while requiring only 0.1% randomly annotated points for training, greatly reducing annotation cost and effort. The code is available at https://github.com/QingyongHu/SQN.