Pamba: Enhancing Global Interaction in Point Clouds via State Space Model

2024-06-25

AI Summary

To address high computational complexity, limited long-range dependency modeling, and poor adaptability of point cloud serialization in Transformer-based 3D point cloud semantic segmentation, this paper proposes Pamba—the first state space model (SSM)-based architecture with linear complexity. Our method introduces: (1) a novel multi-path point cloud serialization strategy that explicitly satisfies the causal constraint of SSMs; (2) the ConvMamba module, which integrates convolutional operations to enhance local geometric modeling and bidirectional contextual awareness; and (3) an end-to-end point cloud feature encoding and fusion framework. Extensive experiments on ScanNet v2, ScanNet200, S3DIS, and nuScenes demonstrate consistent superiority over existing methods, achieving simultaneous gains in accuracy and efficiency. These results validate the effectiveness and scalability of SSMs for large-scale point cloud understanding.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Transformers have demonstrated impressive results for 3D point cloud semantic segmentation. However, the quadratic complexity of transformer makes computation costs high, limiting the number of points that can be processed simultaneously and impeding the modeling of long-range dependencies between objects in a single scene. Drawing inspiration from the great potential of recent state space models (SSM) for long sequence modeling, we introduce Mamba, an SSM-based architecture, to the point cloud domain and propose Pamba, a novel architecture with strong global modeling capability under linear complexity. Specifically, to make the disorderness of point clouds fit in with the causal nature of Mamba, we propose a multi-path serialization strategy applicable to point clouds. Besides, we propose the ConvMamba block to compensate for the shortcomings of Mamba in modeling local geometries and in unidirectional modeling. Pamba obtains state-of-the-art results on several 3D point cloud segmentation tasks, including ScanNet v2, ScanNet200, S3DIS and nuScenes, while its effectiveness is validated by extensive experiments.

Problem

• 3D Point Clouds
• Semantic Segmentation
• Transformer Optimization

Innovation

• State Space Model
• 3D Point Cloud Segmentation
• ConvMamba Enhancement