Raw point clouds collected from real-world scenes are sparse, incomplete and noisy, posing significant challenges for their integration into automation workflows in construction. Thus, completing plausible and fine-grained point clouds is a critical prerequisite for downstream applications. Current methods primarily focus on learning patch-level features and modeling their relationships for inferring complete object shapes. However, the significant disparity between real-world scenarios and clean synthetic datasets limits their representation ability of local structures, especially when facing noises and irregular missing patterns. This paper proposes a semantic keypoint guided completion network (SKPNet) to enhance the generalization ability of point cloud completion in diverse construction scenarios in a semantic-guided manner. The key insight is to build a connection between the object geometric structure and its global semantic feature, which is more robust to point-level disruptions. Accordingly, a semantic keypoint generation module is developed to learn representative keypoints based on the global semantic vector encoded from the input points. These keypoints then serve as the control points for searching the neighboring point-level features with rich local pattern information, simultaneously filtering out the noises during the process. By progressively incorporating multi-scale point-level features, this paper gradually refines and upsamples the keypoints to the final fine-grained completion. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the competitive and robust performance of SKPNet in completing high-quality shapes.

中文翻译：

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学习不同点云完成的语义关键点


从真实场景收集的原始点云稀疏、不完整且噪声大，这给它们集成到施工中的自动化工作流程带来了重大挑战。因此，完成合理和精细的点云是下游应用的关键先决条件。当前方法主要侧重于学习 patch 级特征并对其关系进行建模以推断完整的对象形状。然而，真实场景和干净的合成数据集之间的显着差异限制了它们对局部结构的表示能力，尤其是在面对噪声和不规则缺失模式时。该文提出一种语义关键点引导完成网络 （SKPNet），以语义引导的方式增强点云完成在不同施工场景中的泛化能力。关键的见解是在对象几何结构与其全局语义特征之间建立联系，该特征对点级中断更健壮。因此，开发了一个语义关键点生成模块，以基于输入点编码的全局语义向量来学习代表性关键点。然后，这些关键点将作为控制点，搜索具有丰富局部模式信息的相邻点级特征，同时在此过程中过滤掉噪声。通过逐步整合多尺度的点级特征，该文逐步细化和升采样关键点，最终实现细粒度完成。在合成数据集和真实数据集上进行的综合实验表明，SKPNet 在完成高质量形状方面具有竞争力和稳健性。