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Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy (2403.06467v2)

Published 11 Mar 2024 in cs.CV

Abstract: 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.

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Citations (36)
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Summary

  • The paper introduces Point Mamba, a novel SSM-based architecture that integrates an octree ordering strategy to manage irregular point cloud data.
  • It achieves linear time complexity and outperforms transformer models, recording 93.4% accuracy on ModelNet40 and 75.7% mIoU on ScanNet.
  • The approach extends SSM applications to 3D point cloud processing, paving the way for efficient use in robotics, AR, and autonomous navigation.

Overview of "Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy"

The paper "Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy" addresses the challenge of applying State Space Models (SSMs) to point cloud data, which is intrinsically irregular and disordered. The work introduces a novel architecture called Point Mamba that leverages SSM combined with an octree-based ordering mechanism to establish causality in the processing of point clouds. The researchers demonstrate that Point Mamba achieves linear complexity while outperforming its transformer-based counterparts in both classification and semantic segmentation tasks.

Methodological Insights

State Space Models in Point Clouds: SSMs are recognized for their proficiency in sequence modeling with linear computational complexity. This aspect makes SSMs appealing for applications in language and image domains. However, adapting SSMs to the non-sequential nature of point clouds poses significant challenges. The Point Mamba approach innovatively embeds SSM within a spatially ordered framework, facilitated by an octree-based strategy.

Octree-Based Ordering Strategy: The key to adapting SSMs for point cloud data relies on an octree-based method for point ordering. This technique organizes point cloud data via a z-order sequence that maintains spatial locality while establishing a causal framework. The octree's hierarchical structuring effectively enables the use of SSMs in capturing the global features of point clouds, traditionally constrained by their non-causal nature.

Efficient Backbone Architecture: Point Mamba capitalizes on Mamba's linear time complexity and long-range context capturing ability. Bidirectional selective scanning mechanisms further enhance the model's adaptability to sequence mappings, supporting efficient global feature extraction without partitioning the point data into local windows. Such efficiencies promise smaller parameter footprints and faster operations compared to transformer-based models.

Key Results

The experimental evaluation of Point Mamba is conducted on the ModelNet40 and ScanNet datasets, widely used benchmarks in the 3D point cloud domain. Point Mamba achieves:

  • Classification Accuracy: 93.4% on ModelNet40, surpassing transformer-based architectures.
  • Semantic Segmentation Performance: A mean Intersection over Union (mIoU) of 75.7% on the ScanNet dataset, illustrating its competitive advantage in processing large-scale point clouds.

Implications and Future Directions

The development of Point Mamba suggests several implications for both theoretical and practical facets of point cloud processing:

  • Practical Applications: The enhanced efficiency and accuracy of Point Mamba could influence a range of applications, including autonomous navigation, robotic vision, and augmented reality, where point cloud data is prevalent.
  • SSM as a Generic Backbone: This work validates SSM's potential as a backbone for point cloud data, expanding its applicability beyond traditional sequence tasks. The success of Point Mamba may inspire further exploration into SSM-based architectures across diverse datasets and domains.
  • Potential for Scaling: Given the linear complexity, future work may explore scaling Point Mamba to handle even larger datasets or enhance its capabilities for dynamic scenarios in real-world environments.

Point Mamba presents a significant innovation in 3D point cloud analysis, harnessing the capabilities of SSMs in a novel structural approach. Its potential for high efficiency and accuracy marks a promising direction for future research in AI and 3D data processing applications.

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