用几何距离提升点云补全的结构一致性
Manifold-Aware Point Cloud Completion via Geodesic-Attentive Hierarchical Feature Learning
- 引入测地距离估算模块，捕捉点云内在曲面结构
- 基于测地邻近关系构建注意力机制，提升重建精度
- 适合需要高几何保真的3D重建任务
点云补全旨在从部分或稀疏的3D观测中恢复几何一致的形状。尽管近期方法在全局形状重建上取得较好效果，但通常依赖欧氏距离，忽视点云的非线性几何结构，导致几何一致性不足和语义模糊。本文提出一种流形感知的点云补全框架，将非线性几何信息显式融入特征学习全过程。提出两个关键模块：测地距离近似器（GDA），用于估计点间测地距离以捕获潜在流形拓扑；流形感知特征提取器（MAFE），利用基于测地距离的k-NN分组与测地关系注意力机制，引导层次化特征提取。通过集成测地感知的关系注意力，本方法提升了重建点云的语义一致性和结构保真度。在基准数据集上的大量实验表明，该方法在重建质量上持续优于现有最优方法。
原文摘要 · Abstract (English)
Point cloud completion seeks to recover geometrically consistent shapes from partial or sparse 3D observations. Although recent methods have achieved reasonable global shape reconstruction, they often rely on Euclidean proximity and overlook the intrinsic nonlinear geometric structure of point clouds, resulting in suboptimal geometric consistency and semantic ambiguity. In this paper, we present a manifold-aware point cloud completion framework that explicitly incorporates nonlinear geometry information throughout the feature learning pipeline. Our approach introduces two key modules: a Geodesic Distance Approximator (GDA), which estimates geodesic distances between points to capture the latent manifold topology, and a Manifold-Aware Feature Extractor (MAFE), which utilizes geodesic-based $k$-NN groupings and a geodesic-relational attention mechanism to guide the hierarchical feature extraction process. By integrating geodesic-aware relational attention, our method promotes semantic coherence and structural fidelity in the reconstructed point clouds. Extensive experiments on benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods in reconstruction quality.
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