Hyper-PCN：基于超图的高阶相关性建模点云补全
Hyper-PCN: Hypergraph-Based Point Cloud Completion via High-Order Correlation Modeling
用超图显式建模点云中超越成对关系的高阶相关性，并通过锚点式两阶段采样构建协作超图，为点云补全及其他点云密集预测任务提供了一种可复用的高阶关系建模模块。
原文依据 leverages hypergraphs to explicitly model complex, higher-order correlations within incomplete inputs
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论点云补全是三维计算机视觉中一个重要而富有挑战性的问题，其目标是从部分点云中重建出完整且稠密的三维形状。尽管基于 Transformer 和基于几何的方法已取得显著进展，但它们往往难以捕捉点云中固有的复杂高阶相关性。为解决这一局限，我们提出 Hyper-PCN，一个利用超图显式建模不完整输入中复杂高阶相关性以实现更精确补全的点云补全框架。该框架包含两个关键模块：超图精化堆栈（Hyper Refinement Stack），旨在通过一系列超图学习阶段逐步捕捉由粗到细的高阶相关性；以及基于锚点的超图神经网络（Anchor-based Hypergraph Neural Network），其采用两阶段采样策略构建协作超图，从而确保对全局结构的稳健建模。在多个数据集上的大量实验表明，我们的方法持续优于最优方法。
英文原文摘要 1046 字符
Point cloud completion is an important yet challenging problem in 3D computer vision, which aims to reconstruct complete and dense 3D shapes from partial point clouds. Although transformer-based and geometry-based approaches have made significant progress, they often struggle to capture the complex, high-order correlations inherent in point clouds. To address this limitation, we propose Hyper-PCN, a point cloud completion framework that leverages hypergraphs to explicitly model complex, higher-order correlations within incomplete inputs for more accurate completion. It comprises two key modules: Hyper Refinement Stack, designed to progressively capture coarse-to-fine high-order correlations through a series of hypergraph learning stages, and Anchor-based Hypergraph Neural Network, which employs a two-stage sampling strategy to construct collaborative hypergraphs, ensuring robust modeling of global structures. Extensive experiments on multiple datasets demonstrate that our approach consistently outperforms state-of-the-art methods.