AI 中文总结
针对点云补全由粗到精范式中局部细节重构不足的问题，提出扩散增强细化模块SUMI，可集成到现有模型，在多个基准数据集上实现性能提升。
AI 中文摘要
点云补全通常遵循由粗到精的范式，即先预测低密度的粗形状，再将其上采样至目标分辨率。尽管近期方法在全局结构恢复上有所改进，但精细阶段常受限于简单的上采样操作，且与粗结构特征的交互不足，导致局部细节重构颇具挑战。我们提出SUMI，一种用于由粗到精点云补全的扩散增强细化模块。与现有将扩散作为独立点生成器的扩散型补全方法不同，SUMI将带噪几何特征注入与粗结构特征的交叉注意力中，通过反向去噪在保留全局一致性的同时细化局部几何。SUMI还可作为灵活的细化模块集成到现有由粗到精模型中。在PCN、ShapeNet-55/34和MVP上的实验表明，该方法较强基线取得了一致提升：在PCN上实现了最佳的整体CD和F1分数，在ShapeNet-55上将CD降低最多16.1%，在所有输出密度下均获得MVP上的最佳CD。
英文摘要
Point cloud completion commonly follows a coarse-to-fine paradigm, where a low-density coarse shape is first predicted and then upsampled to the target resolution. Although recent methods have improved global structure recovery, the fine stage often remains limited by simple upsampling and insufficient interaction with coarse structural features, making local detail reconstruction challenging. We propose SUMI, a diffusion-enhanced refinement module for coarse-to-fine point cloud completion. Unlike prior diffusion-based completion methods that use diffusion as a standalone point generator, SUMI injects noisy geometric features into cross-attention with coarse structural features, enabling reverse denoising to refine local geometry while preserving global consistency. SUMI can also be integrated into existing coarse-to-fine models as a flexible refinement module. Experiments on PCN, ShapeNet-55/34, and MVP demonstrate consistent improvements over strong baselines. SUMI achieves the best overall CD and F1-score on PCN, reduces CD by up to 16.1% on ShapeNet-55, and obtains the best CD across all output densities on MVP.
