DM3D：通过偏移引导的可微扫描实现点云理解的可变形Mamba
DM3D: Dynamic Mamba via Offset-Guided Feature Resampling for Point Cloud Understanding
AI总结：
DM3D提出了一种可变形Mamba架构，通过偏移引导的可微扫描机制实现点云的结构自适应序列化，结合可变形空间采样和可微重新排序，提升点云理解性能。
摘要：
状态空间模型（SSMs）在长序列建模中表现出巨大潜力，但其依赖于输入顺序与点云不规则性质相冲突。现有方法通常依赖于预定义的序列化方案，其固定扫描模式无法适应多样的几何结构。为此，我们提出了DM3D，一种用于点云理解的可变形Mamba架构。具体而言，DM3D引入了偏移引导的可微扫描机制，联合执行重采样和重新排序。可变形空间重采样（DSR）通过自适应重采样局部特征增强结构意识，而基于高斯的可微重新排序（GDR）实现了序列化顺序的端到端优化。我们进一步引入了连续性感知的状态更新（CASU）机制，该机制根据局部几何连续性调节状态更新。此外，三路径融合模块促进了不同SSM分支之间的互补交互。这些设计共同实现了点云的结构自适应序列化。在基准数据集上的广泛实验表明，DM3D在分类、少样本学习和部分分割任务中实现了最先进的或高度竞争的结果，验证了自适应序列化在点云理解中的有效性。
英文摘要：
State Space Models (SSMs) model long token sequences of point cloud with linear complexity, but require an unordered point cloud to be serialized. Existing methods mainly address this requirement by designing or learning a better token order. Even a well-constructed order, however, cannot preserve every local relation on an irregular 3D surface: a fixed sequence may still mix points that are close in index but distant in 3D or belong to different object parts. We propose DM3D, a dynamic Mamba architecture that preserves the base token order while adapting local feature support and state propagation. First, according to local feature context, DM3D learns spatial and sequence offsets without constructing a global permutation. Then, spatial offsets adjust the sampling anchors in 3D space, whereas sequence offsets guide feature resampling within a local sequence window, which lets different slots draw from overlapping local supports while retaining their identities. This design preserves the global prior of the original traversal, allowing each token to aggregate a more suitable local context. Second, the state update is modulated by the 3D distance between points at adjacent sequence positions, thereby reducing information propagation when these points are spatially far apart. DM3D reaches 95.2\% accuracy on the ModelNet40, 93.3\% accuracy on the PB\_T50\_RS split of ScanObjectNN, and 84.8\% class mIoU on ShapeNetPart. Extensive experiments on benchmark datasets show that DM3D achieves strong and competitive performance, validating the effectiveness of local feature adaptation for point cloud understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
