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.

翻译：点云分析因深度学习而取得了显著进展。尽管先前的基于Transformer的方法在建模该任务的长程依赖关系方面表现出色，但其计算需求巨大。相反，Mamba提供了更高的效率，但与基于Transformer的方法相比，其潜力有限。在本研究中，我们提出了PoinTramba，这是一个开创性的混合框架，它将Transformer的分析能力与Mamba卓越的计算效率协同结合，以增强点云分析。具体而言，我们的方法首先将点云分割成组，其中Transformer细致地捕捉复杂的组内依赖关系并生成组嵌入，而这些组嵌入之间的组间关系将由高效的Mamba架构同时且巧妙地捕捉，从而确保全面的分析。与先前的Mamba方法不同，我们引入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。这种创新策略基于计算出的重要性分数智能地对组嵌入进行重新排序，显著提升了Mamba的性能并优化了整体分析流程。我们的框架通过无缝集成这些先进技术，在计算效率与分析性能之间实现了卓越的平衡，标志着点云分析领域的一次重大飞跃。在ScanObjectNN、ModelNet40和ShapeNetPart等数据集上进行的大量实验证明了我们方法的有效性，并在点云识别任务上建立了新的最先进分析基准。该范式首次利用了Transformer和Mamba架构的联合优势，为该领域树立了新的标准。代码可在https://github.com/xiaoyao3302/PoinTramba获取。