Weakly Supervised Point Cloud Segmentation by Combining Active Learning Annotation and Multi-Consistency Mechanism

In recent years, fully supervised learning based semantic segmentation algorithms for point clouds have achieved significant advancements. However, a major limitation of these traditional algorithms is their reliance on extensive labeled datasets. This impedes their practical applicability. To overcome this obstacle, this paper proposes a novel point cloud semantic segmentation framework based on weakly supervised learning. This framework is designed to segment point cloud data both efficiently and accurately, even with a limited budget (0.1%) of labeled data. The proposed approach initiates with an active learning annotation strategy. This strategy involves computing the uncertainty scores of each point and ranking them, consequently selecting the top-K points for labeling based on the labeling budget. Furthermore, this paper developed a weakly supervised learning network. This network is enhanced by the calculation of multiple consistency losses to enhance the network's performance. Experimental results demonstrate that with a mere 0.1% labeling ratio, the proposed framework achieves a mean Intersection over Union (mIoU) of 70.3% on the NPM3D dataset.