从参考中获益：检索增强的跨模态点云补全
Benefit from Reference: Retrieval-Augmented Cross-modal Point Cloud Completion
AI总结：
本文提出检索增强的跨模态点云补全框架，通过结构共享特征编码器和渐进式检索增强生成器融合相似参考样本的先验信息，提升细粒度生成与泛化能力。
摘要：
基于不完整点云补全整个三维结构是一项具有挑战性的任务，尤其是当残缺点云缺乏典型结构特征时。近年来基于跨模态学习的方法尝试引入实例图像来辅助结构特征学习，但它们仍然专注于每个特定输入类别，限制了其生成能力。在本工作中，我们提出了一种新颖的检索增强点云补全框架。其核心思想是将跨模态检索引入补全任务，从相似的参考样本中学习结构先验信息。具体来说，我们设计了一个结构共享特征编码器（SSFE），用于联合提取跨模态特征并重建参考特征作为先验。得益于编码器中的双通道控制门，参考样本中的相关结构特征得到增强，无关信息干扰得到抑制。此外，我们提出了一种渐进式检索增强生成器（PRAG），采用分层特征融合机制，将参考先验信息与输入特征从全局到局部进行整合。通过在多个数据集和真实场景上的广泛评估，我们的方法展示了其在生成细粒度点云方面的有效性，以及在处理稀疏数据和未见类别时的泛化能力。
英文摘要：
Completing the whole 3D structure based on an incomplete point cloud is a challenging task, particularly when the residual point cloud lacks typical structural characteristics. Recent methods based on cross-modal learning attempt to introduce instance images to aid the structure feature learning. However, they still focus on each particular input class, limiting their generation abilities. In this work, we propose a novel retrieval-augmented point cloud completion framework. The core idea is to incorporate cross-modal retrieval into completion task to learn structural prior information from similar reference samples. Specifically, we design a Structural Shared Feature Encoder (SSFE) to jointly extract cross-modal features and reconstruct reference features as priors. Benefiting from a dual-channel control gate in the encoder, relevant structural features in the reference sample are enhanced and irrelevant information interference is suppressed. In addition, we propose a Progressive Retrieval-Augmented Generator (PRAG) that employs a hierarchical feature fusion mechanism to integrate reference prior information with input features from global to local. Through extensive evaluations on multiple datasets and real-world scenes, our method shows its effectiveness in generating fine-grained point clouds, as well as its generalization capability in handling sparse data and unseen categories.
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。
