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Point Cloud Mamba: Point Cloud Learning via State Space Model (2403.00762v4)

Published 1 Mar 2024 in cs.CV

Abstract: Recently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying such architecture to more efficiently and effectively model point cloud data globally with linear computational complexity. In particular, for the first time, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs). To enable Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent. Consistent Traverse Serialization yields six variants by permuting the order of \textit{x}, \textit{y}, and \textit{z} coordinates, and the synergistic use of these variants aids Mamba in comprehensively observing point cloud data. Furthermore, to assist Mamba in handling point sequences with different orders more effectively, we introduce point prompts to inform Mamba of the sequence's arrangement rules. Finally, we propose positional encoding based on spatial coordinate mapping to inject positional information into point cloud sequences more effectively. Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanObjectNN, ModelNet40, ShapeNetPart, and S3DIS datasets. It is worth mentioning that when using a more powerful local feature extraction module, our PCM achieves 79.6 mIoU on S3DIS, significantly surpassing the previous SOTA models, DeLA and PTv3, by 5.5 mIoU and 4.9 mIoU, respectively.

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Citations (12)
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Summary

  • The paper introduces a novel framework that converts 3D point clouds into 1D sequences via Consistent Traverse Serialization for robust spatial analysis.
  • It employs order prompts and spatial coordinate-based positional encoding to enhance model interpretability and capture intricate spatial relationships.
  • The PCM model achieves state-of-the-art performance on benchmark datasets, demonstrating scalability with its efficient PCM-Tiny variant.

Unveiling Point Cloud Mamba: A Leap Forward in 3D Point Cloud Analysis with State Space Models

Introduction to Point Cloud Mamba

The recent work on "Point Cloud Mamba (PCM)" presents a novel approach to 3D point cloud analysis, leveraging the intrinsic capabilities of Mamba, a state space model (SSM), to surpass the performance benchmarks set by existing point-based methods. By ingeniously converting 3D point cloud data into 1D sequences through the newly introduced Consistent Traverse Serialization (CTS), PCM enables the Mamba model to process complex spatial data efficiently. This architecture is further enhanced with the novel concepts of order prompts and spatial coordinate-based positional encoding, collectively facilitating precise and comprehensive point cloud analysis.

Breaking Down the Technical Contributions

The authors highlight three key technical advancements in their work that culminate in the formulation of the PCM framework:

  • Introduction of Consistent Traverse Serialization:

    CTS is instrumental in transforming 3D point clouds into 1D sequences which are digestible by the Mamba model. The approach ensures spatial adjacency is preserved in the serialization process, which is critical for maintaining the spatial coherence of the point cloud. This methodology yields six variants by permuting the order of x, y, and z coordinates, enriching the model's ability to capture the essence of the spatial data from multiple perspectives.

  • Implementation of Order Prompts:

    To enable the Mamba model to discern among differently serialized point sequences, the authors propose the use of order prompts. These prompts act as identifiers, providing the model with the necessary context to understand the arrangement of the input sequences, thus improving the model's interpretability and performance on point cloud data.

  • Spatial Coordinate-Based Positional Encoding:

    Recognizing the limitations of conventional positional embedding techniques when applied to irregular point cloud data, the paper proposes a novel approach that maps the spatial coordinates of points to positional embeddings. This method more accurately reflects the spatial relationships within the point cloud, enhancing the model's ability to process and analyze the data.

Achievements and Outcomes

The PCM model demonstrates state-of-the-art performance on three benchmark datasets: ScanObjectNN, ModelNet40, and ShapeNetPart. Notably, PCM achieves a notable improvement over the leading point-based method, PointNeXt, on the ShapeNetPart dataset. These results are attributed to the combined local and global modelling framework, which accurately captures the nuances of 3D spatial data. Moreover, the implementation of PCM-Tiny, a scaled-down version of PCM, showcases the potential for achieving high-performance point cloud analysis with reduced computational requirements.

Implications and Future Directions

The introduction of PCM represents a significant advancement in 3D point cloud analysis. By leveraging the power of Mamba for global feature modeling and integrating novel techniques for sequence serialization and positional encoding, PCM sets new performance benchmarks for the field. The success of PCM opens the door for further exploration into the application of state space models in other domains of 3D data analysis and beyond.

The potential for refining and extending the PCM architecture is vast. Future work could explore the integration of additional data modalities, such as textures or colors, into the PCM framework, or the adaptation of the model for dynamic point cloud data, such as that obtained from LiDAR scans of moving objects. Additionally, optimizing the model's performance for real-time applications presents an exciting avenue for research, potentially facilitating advancements in autonomous navigation, robotics, and augmented reality.

Concluding Remarks

The Point Cloud Mamba framework ushers in a new era in point cloud analysis, demonstrating the untapped potential of state space models in understanding and interpreting complex spatial data. Through its innovative approach and remarkable performance, PCM not only advances the field of point cloud analysis but also establishes a foundation for future exploration and innovation in 3D data processing.

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