论文标题

通过图匹配的点云语义分割的无监督域的适应

Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching

论文作者

Bian, Yikai, Hui, Le, Qian, Jianjun, Xie, Jin

论文摘要

无监督的域对点云语义细分的适应性引起了人们的关注,因为它在学习中使用未标记的数据在学习方面有效。大多数现有方法都使用全局级特征对齐方式将知识从源域转移到目标域,这可能会导致特征空间的语义歧义。在本文中,我们提出了一个基于图形的框架,以探索两个域之间的局部特征对齐,可以在适应过程中保留语义歧视。具体而言,为了提取本地级特征,我们首先在两个域上动态构建本地特征图,并使用来自源域的图形构建存储库。特别是,我们使用最佳传输来生成图形匹配对。然后,基于分配矩阵,我们可以将两个域之间的特征分布与基于图的本地特征损失对齐。此外,我们考虑了不同类别的特征之间的相关性,并制定了类别引导的对比损失,以指导分割模型以学习目标域上的区分特征。对不同的合成到现实和真实域的适应情景进行了广泛的实验表明,我们的方法可以实现最先进的性能。

Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data. Most of existing methods use global-level feature alignment to transfer the knowledge from the source domain to the target domain, which may cause the semantic ambiguity of the feature space. In this paper, we propose a graph-based framework to explore the local-level feature alignment between the two domains, which can reserve semantic discrimination during adaptation. Specifically, in order to extract local-level features, we first dynamically construct local feature graphs on both domains and build a memory bank with the graphs from the source domain. In particular, we use optimal transport to generate the graph matching pairs. Then, based on the assignment matrix, we can align the feature distributions between the two domains with the graph-based local feature loss. Furthermore, we consider the correlation between the features of different categories and formulate a category-guided contrastive loss to guide the segmentation model to learn discriminative features on the target domain. Extensive experiments on different synthetic-to-real and real-to-real domain adaptation scenarios demonstrate that our method can achieve state-of-the-art performance.

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