Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy
<|reference_start|>Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy: Recently, state space model (SSM) has gained great attention due to its promising performance, linear complexity, and long sequence modeling ability in both language and image domains. However, it is non-trivial to extend SSM to the point cloud field, because of the causality requirement of SSM and the disorder and irregularity nature of point clouds. In this paper, we propose a novel SSM-based point cloud processing backbone, named Point Mamba, with a causality-aware ordering mechanism. To construct the causal dependency relationship, we design an octree-based ordering strategy on raw irregular points, globally sorting points in a z-order sequence and also retaining their spatial proximity. Our method achieves state-of-the-art performance compared with transformer-based counterparts, with 93.4% accuracy and 75.7 mIOU respectively on the ModelNet40 classification dataset and ScanNet semantic segmentation dataset. Furthermore, our Point Mamba has linear complexity, which is more efficient than transformer-based methods. Our method demonstrates the great potential that SSM can serve as a generic backbone in point cloud understanding. Codes are released at https://github.com/IRMVLab/Point-Mamba.<|reference_end|>
@article{liu2024point,
    title={Point Mamba: A Novel Point Cloud Backbone Based on State Space Model
  with Octree-Based Ordering Strategy},
    author={Jiuming Liu, Ruiji Yu, Yian Wang, Yu Zheng, Tianchen Deng, Weicai Ye,
  Hesheng Wang},
    journal={arXiv preprint arXiv:2403.06467},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06467},
    primaryClass={cs.CV}
}