Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model

• URL: http://arxiv.org/abs/2404.14966v2
• Date: Mon, 2 Sep 2024 12:55:04 GMT
• Title: Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
• Authors: Xu Han, Yuan Tang, Zhaoxuan Wang, Xianzhi Li,
• Abstract summary: Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. We present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction.
• Score: 18.30032389736101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the newly proposed Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. However, the straightforward adoption of Mamba does not achieve satisfactory performance on point cloud tasks. In this work, we present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction, achieving superior performance, high efficiency, and scalability potential. Specifically, we propose a simple yet effective Local Norm Pooling (LNP) block to extract local geometric features. Additionally, to obtain better global features, we introduce a bidirectional SSM (bi-SSM) with both a token forward SSM and a novel backward SSM that operates on the feature channel. Extensive experimental results show that Mamba3D surpasses Transformer-based counterparts and concurrent works in multiple tasks, with or without pre-training. Notably, Mamba3D achieves multiple SoTA, including an overall accuracy of 92.6% (train from scratch) on the ScanObjectNN and 95.1% (with single-modal pre-training) on the ModelNet40 classification task, with only linear complexity. Our code and weights are available at https://github.com/xhanxu/Mamba3D.

Related papers

• NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs [9.978766637766373]
  We introduce a method to convert point clouds into 1D sequences that maintain 3D spatial structure with no need for data replication. Our method does not require positional embeddings and allows for shorter sequence lengths while still achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-31T18:58:40Z)
• Mamba24/8D: Enhancing Global Interaction in Point Clouds via State Space Model [37.375866491592305]
  We introduce Mamba, a SSM-based architecture, to the point cloud domain. We propose Mamba24/8D, which has strong global modeling capability under linear complexity. Mamba24/8D obtains state of the art results on several 3D point cloud segmentation tasks.
  arXiv  Detail & Related papers  (2024-06-25T10:23:53Z)
• Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection [59.34834815090167]
  Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. We present a Voxel SSM, which employs a group-free strategy to serialize the whole space of voxels into a single sequence.
  arXiv  Detail & Related papers  (2024-06-15T17:45:07Z)
• PointABM:Integrating Bidirectional State Space Model with Multi-Head Self-Attention for Point Cloud Analysis [8.500020888201231]
  Mamba, based on state space model (SSM) with its linear complexity and great success in classification provide its superiority in 3D point cloud analysis. Transformer has emerged as one of the most prominent and successful architectures for point cloud analysis. We present PointABM, a hybrid model that integrates the Mamba and Transformer architectures for enhancing local feature to improve performance of 3D point cloud analysis.
  arXiv  Detail & Related papers  (2024-06-10T07:24:22Z)
• Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs. Specifically, we introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer encoder for spatial feature extraction. We benchmark three practical sports game datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Point Cloud Mamba: Point Cloud Learning via State Space Model [73.7454734756626]
  We show that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs) In particular, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs) Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanNN, ModelNet40, ShapeNetPart, and S3DIS datasets.
  arXiv  Detail & Related papers  (2024-03-01T18:59:03Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  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.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)
• CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning [81.85951026033787]
  We set transformers in this work and incorporate them into a hierarchical framework for shape classification and part and scene segmentation. We also compute efficient and dynamic global cross attentions by leveraging sampling and grouping at each iteration. The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods.
  arXiv  Detail & Related papers  (2022-07-31T21:39:15Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.