用自适应图结构补全点云，重点强化细节区域的重建效果。
DFG-PCN: Point Cloud Completion with Degree-Flexible Point Graph
- 根据几何复杂度动态调整点的连接数，聚焦关键结构区域。
- 在ScanNet、ShapeNet等数据集上优于当前最优方法，细节还原更准确。
- 适合需要高精度点云重建的应用，如3D建模与自动驾驶。
点云补全是恢复因遮挡和传感器分辨率限制导致不完整点云的重要任务。传统方法依赖固定局部区域划分（如k近邻），难以应对形状不同区域间几何复杂度分布不均的问题，导致表征效率低且重建效果不佳，尤其在细粒度结构或断续区域表现差。本文提出一种名为度灵活点图补全网络（DFG-PCN）的点云补全框架。该方法通过结合特征变化与曲率的细节感知度量，自适应地为节点分配不同连接数，以关注结构重要区域。进一步引入几何感知图融合模块，利用曼哈顿距离进行边聚合，并实现局部与全局特征的细节引导融合，提升表征能力。在多个基准数据集上的大量实验表明，本方法持续优于现有最先进方法。
原文摘要 · Abstract (English)
Point cloud completion is a vital task focused on reconstructing complete point clouds and addressing the incompleteness caused by occlusion and limited sensor resolution. Traditional methods relying on fixed local region partitioning, such as k-nearest neighbors, which fail to account for the highly uneven distribution of geometric complexity across different regions of a shape. This limitation leads to inefficient representation and suboptimal reconstruction, especially in areas with fine-grained details or structural discontinuities. This paper proposes a point cloud completion framework called Degree-Flexible Point Graph Completion Network (DFG-PCN). It adaptively assigns node degrees using a detail-aware metric that combines feature variation and curvature, focusing on structurally important regions. We further introduce a geometry-aware graph integration module that uses Manhattan distance for edge aggregation and detail-guided fusion of local and global features to enhance representation. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art approaches.
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