  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.
