PoinTramba：一种用于点云分析的Transformer-Mamba混合框架
PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis
AI总结：
针对点云分析中Transformer计算量大、Mamba性能有限的问题，提出融合二者的PoinTramba混合框架，结合组内Transformer建模与组间Mamba建模，辅以双向重要性感知排序策略，在多个数据集上实现点云识别新SOTA。
摘要：
点云分析在深度学习的推动下已取得显著进展，尽管以往基于Transformer的方法擅长在该任务中建模长距离依赖关系，但其计算需求巨大。与之相反，Mamba的效率更高，但与基于Transformer的方法相比潜力有限。本研究提出PoinTramba这一开创性混合框架，融合Transformer的分析能力与Mamba卓越的计算效率，以提升点云分析效果。具体而言，该方法首先将点云划分为多个组，由Transformer精准捕捉组内复杂依赖关系并生成组嵌入，同时高效的Mamba架构会灵活捕捉组间关系，确保分析的全面性。与以往的Mamba方法不同，我们提出了一种双向重要性感知排序（BIO）策略，以应对随机排序效应带来的挑战。该创新策略根据计算得到的重要性得分对组嵌入进行智能重排序，显著提升了Mamba的性能，并优化了整体分析流程。通过无缝整合这些先进技术，我们的框架在计算效率与分析性能之间实现了极佳的平衡，标志着点云分析领域的重大进步。在ScanObjectNN、ModelNet40和ShapeNetPart等数据集上开展的大量实验证明了该方法的有效性，在点云识别任务上树立了新的最先进分析基准。这一范式首次利用了Transformer与Mamba两种架构的综合优势，为该领域确立了新的标准。代码可在https://github.com/xiaoyao3302/PoinTramba获取。
英文摘要：
Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial. Conversely, the Mamba offers greater efficiency but shows limited potential compared with Transformer-based methods. In this study, we introduce PoinTramba, a pioneering hybrid framework that synergies the analytical power of Transformer with the remarkable computational efficiency of Mamba for enhanced point cloud analysis. Specifically, our approach first segments point clouds into groups, where the Transformer meticulously captures intricate intra-group dependencies and produces group embeddings, whose inter-group relationships will be simultaneously and adeptly captured by efficient Mamba architecture, ensuring comprehensive analysis. Unlike previous Mamba approaches, we introduce a bi-directional importance-aware ordering (BIO) strategy to tackle the challenges of random ordering effects. This innovative strategy intelligently reorders group embeddings based on their calculated importance scores, significantly enhancing Mamba's performance and optimizing the overall analytical process. Our framework achieves a superior balance between computational efficiency and analytical performance by seamlessly integrating these advanced techniques, marking a substantial leap forward in point cloud analysis. Extensive experiments on datasets such as ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our approach, establishing a new state-of-the-art analysis benchmark on point cloud recognition. For the first time, this paradigm leverages the combined strengths of both Transformer and Mamba architectures, facilitating a new standard in the field. The code is available at https://github.com/xiaoyao3302/PoinTramba.
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。
