Abstract
Current semantic segmentation approaches for point cloud scenes heavily rely on manual labeling, while research on unsupervised semantic segmentation methods specifically for raw point clouds is still in its early stages. Unsupervised point cloud learning poses significant challenges due to the absence of annotation information and the lack of pre-training. The development of effective strategies is crucial in this context. In this paper, we propose a novel prototype library-driven unsupervised point cloud semantic segmentation strategy that utilizes Structure Learning and Consistent Reasoning (P-SLCR). First, we propose a Consistent Structure Learning to establish structural feature learning between consistent points and the library of consistent prototypes by selecting high-quality features. Second, we propose a Semantic Relation Consistent Reasoning that constructs a prototype inter-relation matrix between consistent and ambiguous prototype libraries separately. This process ensures the preservation of semantic consistency by imposing constraints on consistent and ambiguous prototype libraries through the prototype inter-relation matrix. Finally, our method was extensively evaluated on the S3DIS, SemanticKITTI, and Scannet datasets, achieving the best performance compared to unsupervised methods. Specifically, the mIoU of 47.1% is achieved for Area-5 of the S3DIS dataset, surpassing the classical fully supervised method PointNet by 2.5%.
Abstract (translated)
目前针对点云场景的语义分割方法严重依赖于人工标注，而专门用于原始点云数据的无监督语义分割方法的研究仍处于初级阶段。由于缺乏注释信息和预训练数据，无监督点云学习面临着重大挑战。在这种背景下，开发有效的策略至关重要。 本文提出了一种新颖的方法：原型库驱动的无监督点云语义分割策略（P-SLCR），该方法结合了结构学习和一致性推理。首先，我们提出了一个一致结构学习模块，通过选择高质量特征，在一致点与一致原型库之间建立结构性特征学习。其次，我们提出了一致性关系推理模块，它分别构建两个独立的原型互相关矩阵：一致原型库和模棱两可原型库之间的矩阵。这一过程确保了语义一致性，通过对一致和模棱两可原型库进行约束来维持原型间的相互关联矩阵。 最后，我们的方法在S3DIS、SemanticKITTI和ScanNet数据集上进行了广泛的评估，在无监督方法中取得了最佳性能。具体而言，在S3DIS数据集的Area-5部分实现了47.1%的mIoU指标，这一成绩甚至超过了经典的完全有监督方法PointNet 2.5个百分点。
URL
https://arxiv.org/abs/2603.06321
