用几何结构引导并行点云补全，兼顾精度与效率
PPC-MT: Parallel Point Cloud Completion with Mamba-Transformer Hybrid Architecture
- 基于PCA将无序点云转为有序子集并行重建
- 在PCN、ShapeNet和KITTI上均超越现有方法
- 融合Mamba与Transformer，兼顾速度与细节
现有点云补全方法难以兼顾高质量重建与计算效率。为此，我们提出PPC-MT，一种基于Mamba-Transformer混合架构的新型并行点云补全框架。该方法引入基于主成分分析（PCA）的并行补全策略，为无序点云施加几何意义结构，将其转化为有序集合并分解为多个子集，通过多头重构器并行重建。这一结构化并行合成范式显著提升点分布均匀性与细节保真度，同时保持高效计算。通过在编码阶段结合Mamba的线性复杂度以实现高效特征提取，在解码阶段利用Transformer建模细粒度多序列关系，PPC-MT有效平衡了效率与重建精度。在PCN、ShapeNet-55/34和KITTI等基准数据集上的大量定量与定性实验表明，该方法在多个指标上优于当前最优方法，验证了所提框架的有效性。
原文摘要 · Abstract (English)
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.
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