QPCR: Weakly Supervised Semantic Segmentation of Large-Scale Point Cloud via Gradual Query Points Component Reasoning

Semantic segmentation of large-scale point clouds under weak supervision is challenging due to the limited annotations. Current methods typically rely on the implicit use of annotation information to supervise the network, thereby constraining the capacity to characterize features at annotation points. In this article, we propose a query points component reasoning (QPCR) framework, which enhances the utilization of annotation information by introducing them into the middle layer, thereby refining the features used for semantic segmentation. First, we propose the semantic annotations component code reasoning (SACC Reasoning) module, which uses annotations of query points to supervise the semantic information of gradual query points. The semantic annotations component code (SACC) Reasoning module can guide the learning of feature representations of semantic annotations at multiple scales. Second, we create the semantic primitive component code reasoning (SPCC Reasoning) module, which addresses the issue of supervising the network using existing annotation point information that may suppress active features. The semantic primitive component code (SPCC) Reasoning module infers semantic primitive labels of query points using the decoding layer and uses them to guide the corresponding coding layer in learning semantic primitives. Finally, our QPCR method achieves state-of-the-art (SOTA) results on public benchmark datasets. Specifically, even with only 1% of annotated points, our QPCR method achieves the highest mIoU accuracy, i.e., 65.4% on S3DIS Area 5, 56.4% on SensatUrban and 55.3% mIoU on SemanticKITTI, respectively.