Existing point cloud completion methods struggle to balance high-quality reconstruction with computational efficiency. To address this, we propose PPC-MT, a novel parallel framework for point cloud completion leveraging a hybrid Mamba-Transformer architecture. Our approach introduces an innovative parallel completion strategy guided by Principal Component Analysis (PCA), which imposes a geometrically meaningful structure on unordered point clouds, transforming them into ordered sets and decomposing them into multiple subsets. These subsets are reconstructed in parallel using a multi-head reconstructor. This structured parallel synthesis paradigm significantly enhances the uniformity of point distribution and detail fidelity, while preserving computational efficiency. By integrating Mamba's linear complexity for efficient feature extraction during encoding with the Transformer's capability to model fine-grained multi-sequence relationships during decoding, PPC-MT effectively balances efficiency and reconstruction accuracy. Extensive quantitative and qualitative experiments on benchmark datasets, including PCN, ShapeNet-55/34, and KITTI, demonstrate that PPC-MT outperforms state-of-the-art methods across multiple metrics, validating the efficacy of our proposed framework.

翻译：现有点云补全方法难以在高质量重建与计算效率之间取得平衡。为解决此问题，我们提出PPC-MT，一种利用混合Mamba-Transformer架构进行点云补全的新型并行框架。我们的方法引入了一种创新的、由主成分分析引导的并行补全策略，该策略对无序点云施加了具有几何意义的结构，将其转换为有序集合并分解为多个子集。这些子集使用多头重建器进行并行重建。这种结构化的并行合成范式显著提升了点分布的均匀性和细节保真度，同时保持了计算效率。通过结合Mamba在编码阶段用于高效特征提取的线性复杂度，以及Transformer在解码阶段对细粒度多序列关系建模的能力，PPC-MT有效地平衡了效率与重建精度。在PCN、ShapeNet-55/34和KITTI等基准数据集上进行的大量定量与定性实验表明，PPC-MT在多项指标上优于现有最先进方法，验证了所提框架的有效性。