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PointMamba: A Simple State Space Model for Point Cloud Analysis

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arxiv 2402.10739 v5 pith:3R5VL342 submitted 2024-02-16 cs.CV

Dingkang Liang , Xin Zhou , Wei Xu , Xingkui Zhu , Zhikang Zou , Xiaoqing Ye , Xiao Tan , Xiang Bai This is my paper · ORCID
classification cs.CV
keywords pointmambapointanalysiscloudcomplexityglobalmodelingsimple
verification ladder T0 review T1 audit T2 compute T3 formal
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Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability. However, the attention mechanism has quadratic complexity, making the design of a linear complexity method with global modeling appealing. In this paper, we propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs. Specifically, our method leverages space-filling curves for effective point tokenization and adopts an extremely simple, non-hierarchical Mamba encoder as the backbone. Comprehensive evaluations demonstrate that PointMamba achieves superior performance across multiple datasets while significantly reducing GPU memory usage and FLOPs. This work underscores the potential of SSMs in 3D vision-related tasks and presents a simple yet effective Mamba-based baseline for future research. The code will be made available at \url{https://github.com/LMD0311/PointMamba}.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.

  2. PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification

    cs.CV 2025-08 conditional novelty 6.0 of 10

    PointDGRWKV applies RWKV-like attention to domain-generalized point cloud classification, adding a geometric token shift and key-distribution alignment, and reports state-of-the-art accuracy on PointDA-10 and PointDG-3to1.

  3. MeshMamba: State Space Models for Articulated 3D Mesh Generation and Reconstruction

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MeshMamba applies Mamba state space models to dense 3D articulated mesh generation and single-image human mesh recovery, reaching over 10,000 vertices with competitive accuracy and faster inference than transformers.

  4. MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A camera-LiDAR 3D detector built around a hybrid local-global Mamba block with height-fidelity LiDAR encoding reports 75.0 NDS on nuScenes validation, outperforming prior transformer-based fusion methods.

  5. Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Semantic Masked Autoencoder uses learned component prototypes to mask complete point cloud parts during pre-training and as prompts during fine-tuning, improving downstream 3D classification and segmentation.

  6. Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning

    cs.CV 2025-06 reject novelty 6.0 of 10

    AsymDSD unifies latent masked point modeling and cross-view invariance self-distillation to learn 3D representations, reporting 90.53% on ScanObjectNN and 93.72% with 930k-shape pretraining.

  7. PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter

    cs.CV 2025-05 conditional novelty 6.0 of 10

    PMA adapts frozen point cloud models by ordering and fusing all intermediate layer features with Mamba, achieving parameter-efficient gains on ScanObjectNN, ModelNet40, and ShapeNetPart.

  8. SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Skeleton-based pretraining plus BatchNorm-only test-time adaptation gives fast, accurate 3D point cloud classification under corruption on ModelNet40-C and ScanObjectNN-C, but not uniformly across all tested benchmarks.

  9. Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution

    cs.CV 2025-06 conditional novelty 5.0 of 10

    SCST reports the best perceptual quality (LPIPS/DISTS) on four synthetic benchmarks and the best no-reference quality scores on the real-world VideoLQ benchmark by adding spatio-temporal Mamba and contrastive ControlN...

  10. Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution

    cs.CV 2025-09 conditional novelty 4.0 of 10

    LFMT, a hybrid Mamba-Transformer network with unidirectional subspace scanning, reports new state-of-the-art light field super-resolution results on five benchmarks.

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