MT-PCR: A Hybrid Mamba-Transformer with Spatial Serialization for Hierarchical Point Cloud Registration

• URL: http://arxiv.org/abs/2506.13183v1
• Date: Mon, 16 Jun 2025 07:49:43 GMT
• Title: MT-PCR: A Hybrid Mamba-Transformer with Spatial Serialization for Hierarchical Point Cloud Registration
• Authors: Bingxi Liu, An Liu, Hao Chen, Jinqiang Cui, Yiqun Wang, Hong Zhang,
• Abstract summary: We propose MT-PCR, the first point cloud registration framework that integrates both Mamba and Transformer modules.<n>We show that MT-PCR outperforms Transformer-based and concurrent state-of-the-art methods in both accuracy and efficiency.
• Score: 15.482436877752331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud registration (PCR) is a fundamental task in 3D computer vision and robotics. Most existing learning-based PCR methods rely on Transformers, 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 (SSMs)-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 this challenge, we propose MT-PCR, the first point cloud registration framework that integrates both 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 input. Additionally, we remove the order indicator module commonly used in Mamba-based sequence modeling, leads to improved performance in our setting. The serialized features are then processed by an optimized Mamba encoder, followed by a Transformer refinement stage. Extensive experiments on multiple benchmarks demonstrate that MT-PCR outperforms Transformer-based and concurrent state-of-the-art methods in both accuracy and efficiency, significantly reducing while GPU memory usage and FLOPs.

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