PC-Adapter：面向点云高效域适应的拓扑感知适配器与校正伪标签
PC-Adapter: Topology-Aware Adapter for Efficient Domain Adaption on Point Clouds with Rectified Pseudo-label
AI总结：
本文提出 PC-Adapter，通过注意力与图卷积双适配器分别建模源域全局形状和目标域局部特征，并以类别感知置信度校正伪标签，在多个点云域适应基准上优于基线。
摘要：
由于物体尺度、传感器角度和自遮挡变化所导致的数据分布偏移，理解真实世界采集的点云颇具挑战。以往工作通过结合自监督学习、自训练和对抗训练等近期学习原则来解决该问题，却带来了显著的计算开销。为实现简洁而强大的点云域适应，我们重新审视了域偏移场景下点云数据的独特挑战，发现源数据全局几何结构的重要性，以及目标伪标签偏向源标签分布的趋势。受这些观察启发，我们提出一种由适配器引导的域适应方法 PC-Adapter：它利用基于注意力的适配器保留源域的全局形状信息，同时通过另一个配备图卷积的适配器学习目标域的局部特征。此外，我们提出一种对分类器偏差具有鲁棒性的新型伪标签策略，通过依据各类别置信度分布调整置信度分数以考虑相对置信度。在 PointDA、GraspNetPC 和 PointSegDA 基准数据集的多种域偏移设置中，我们的方法相较基线展现出优越性。
英文摘要：
Understanding point clouds captured from the real-world is challenging due to shifts in data distribution caused by varying object scales, sensor angles, and self-occlusion. Prior works have addressed this issue by combining recent learning principles such as self-supervised learning, self-training, and adversarial training, which leads to significant computational overhead.Toward succinct yet powerful domain adaptation for point clouds, we revisit the unique challenges of point cloud data under domain shift scenarios and discover the importance of the global geometry of source data and trends of target pseudo-labels biased to the source label distribution. Motivated by our observations, we propose an adapter-guided domain adaptation method, PC-Adapter, that preserves the global shape information of the source domain using an attention-based adapter, while learning the local characteristics of the target domain via another adapter equipped with graph convolution. Additionally, we propose a novel pseudo-labeling strategy resilient to the classifier bias by adjusting confidence scores using their class-wise confidence distributions to consider relative confidences. Our method demonstrates superiority over baselines on various domain shift settings in benchmark datasets - PointDA, GraspNetPC, and PointSegDA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
