MT-PCR：基于空间串行化的混合Mamba-Transformer网络用于点云配准
MT-PCR: Hybrid Mamba-Transformer Network with Spatial Serialization for Point Cloud Registration
AI总结：
本文提出MT-PCR框架，结合Mamba和Transformer模块，通过Z-order曲线串行点云特征以增强空间局部性，提升点云配准的精度与效率。
摘要：
点云配准（PCR）是三维计算机视觉和机器人领域的重要任务。大多数基于学习的PCR方法依赖于Transformer架构，其二次计算复杂度限制了可处理点云的分辨率，导致信息丢失。相比之下，Mamba基于状态空间模型提出，具有线性计算复杂度并保持强大的长距离上下文建模能力。然而，直接应用Mamba到PCR任务效果不佳，因点云数据无序且不规则。为此，本文提出MT-PCR，首个整合Mamba和Transformer模块的点云配准框架。具体而言，通过Z-order空间填充曲线串行点云特征以强制空间局部性，使Mamba更好地建模输入的几何结构。此外，移除Mamba序列建模中常用的顺序指示模块，提升性能。串行特征经优化的Mamba编码器处理后，再通过基于Transformer的特征细化阶段。在多个基准测试中，MT-PCR在精度和效率上均优于基于Transformer和其他最先进的方法，显著减少GPU内存使用和FLOPs。
英文摘要：
Point cloud registration (PCR) is a fundamental task in 3D computer vision and robotics. Most learning-based PCR methods rely on Transformer architectures, which suffer from quadratic computational complexity. This limitation restricts the resolution of point clouds that can be processed, inevitably leading to information loss. In contrast, Mamba, a recently proposed model based on state-space models, achieves linear computational complexity while maintaining strong long-range contextual modeling capabilities. However, directly applying Mamba to PCR tasks yields suboptimal performance due to the unordered and irregular nature of point cloud data. To address these challenges, we propose MT-PCR, the first point cloud registration framework that integrates Mamba and Transformer modules. Specifically, we serialize point cloud features using Z-order space-filling curves to enforce spatial locality, enabling Mamba to better model the geometric structure of the inputs. Additionally, we remove the order-indicator module commonly used in Mamba-based sequence modeling, leading to improved performance in our setting. The serialized features are then processed by an optimized Mamba encoder, followed by a Transformer-based feature refinement stage. Extensive experiments on multiple benchmarks demonstrate that MT-PCR outperforms Transformer-based and other state-of-the-art methods in both accuracy and efficiency, significantly reducing GPU memory usage and FLOPs.
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。
