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3DSMT: A Hybrid Spiking Mamba-Transformer for Point Cloud Analysis

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Point Cloud AnalysisSpiking neural networkSpiking Local Offset AttentionSpiking Mamba Block
Abstract:

The sparse unordered structure of point clouds causes unnecessary computation and energy consumption in deep models. Conventionally, the Transformer architecture is leveraged to model global relationships in point clouds, however, its quadratic complexity restricts scalability. Although the Mamba architecture enables efficient global modeling with linear complexity, it lacks natural adaptability to unordered point clouds. Spiking Neural Network (SNN) is an energy-efficient alternative to Artificial Neural Network (ANN), offering an ultra low-power event-driven paradigm. The inherent sparsity and event-driven characteristics of SNN are highly compatible with the sparse distribution of point clouds. To balance efficiency and performance, we propose a hybrid spiking Mamba-Transformer (3DSMT) model for point cloud analysis. 3DSMT integrates a Spiking Local Offset Attention module to efficiently capture fine-grained local geometric features with a spiking Mamba block designed for unordered point clouds to achieve global feature integration with linear complexity. Experiments show that 3DSMT achieves state-of-the-art performance among SNN-based methods in shape classification, few-shot classification, and part segmentation tasks, significantly reducing computational energy consumption while also outperforming numerous ANN-based models. Our source code is in supplementary material and will be made publicly available

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a hybrid spiking Mamba-Transformer architecture (3DSMT) that combines local attention and Mamba-based global modeling for point cloud analysis. It resides in the Transformer-Based SNN Architectures leaf, which contains only two papers including this one. This is a relatively sparse research direction within the broader taxonomy of 34 papers across multiple branches, suggesting that transformer-style mechanisms in spiking point cloud networks remain an emerging area with limited prior exploration.

The taxonomy reveals that neighboring leaves include Mamba-Based SNN Architectures (one paper) and Convolutional and Graph-Based SNN Architectures (four papers), indicating that most prior work has favored convolutional or graph-based approaches over attention mechanisms. The Transformer-Based leaf's scope explicitly excludes Mamba-only or purely convolutional designs, positioning this work at the boundary between transformer attention and state-space models. The sibling paper in this leaf (Spiking Point Transformer) shares the transformer focus but differs in its treatment of temporal dynamics and memory efficiency, as noted in the taxonomy narrative.

Among 30 candidates examined, none were found to refute the three core contributions: the Spiking Local Offset Attention module (10 candidates, 0 refutable), the Spiking Mamba Block for unordered point clouds (10 candidates, 0 refutable), and the 3DSMT hybrid architecture (10 candidates, 0 refutable). This suggests that within the limited search scope, the specific combination of local spiking attention and Mamba-based global modeling appears novel. However, the search scale is modest, and the taxonomy narrative indicates that related works like Spiking Mamba Transformer and Spiking Point Transformer explore overlapping themes of long-range dependencies and state-space models in spiking point cloud processing.

Based on the top-30 semantic matches and the sparse population of the Transformer-Based SNN Architectures leaf, the work appears to occupy a relatively unexplored niche. The absence of refutable candidates across all contributions within this limited scope is noteworthy, though it does not preclude the existence of relevant prior work outside the examined set. The hybrid design bridging local attention and Mamba blocks represents a distinct architectural choice in a field where most efforts have concentrated on convolutional or graph-based spiking modules.

Taxonomy

Core-task Taxonomy Papers
34
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: energy-efficient point cloud analysis using spiking neural networks. The field has evolved into several distinct branches that reflect different strategies for bringing SNNs to bear on 3D spatial data. Core SNN Architectures for Point Cloud Processing focuses on designing native spiking modules that can handle unordered point sets, often adapting ideas from classical point cloud networks like PointNet or PointCNN into the spiking domain (e.g., Spiking PointNet[7], Spiking PointCNN[8]). ANN-to-SNN Conversion Methods explores how to translate pre-trained artificial neural networks into spiking equivalents with minimal accuracy loss, while Training Optimization and Regularization develops techniques such as temporal regularization and noise injection to improve learning dynamics. Task-Specific Applications and Streaming and Real-Time Processing address practical deployment scenarios, including object detection and event-driven inference. Neuromorphic Hardware and System Integration examines co-design with specialized chips, and Spatio-Temporal Signal Processing Beyond Point Clouds broadens the scope to related modalities like event cameras. Foundational SNN Studies with Point Cloud Examples provides theoretical grounding through works that use point clouds as illustrative benchmarks. Recent activity has concentrated on bridging the gap between transformer-style attention mechanisms and spiking computation, as well as on hybrid architectures that blend convolutional and recurrent elements for temporal coherence. Within the Transformer-Based SNN Architectures branch, Spiking Mamba Transformer[0] and Spiking Point Transformer[2] both explore how to incorporate long-range dependencies and state-space models into spiking point cloud processing, yet they differ in their handling of temporal dynamics and memory efficiency. Nearby works like Point to Spike[3] and Spikepoint[5] emphasize direct encoding strategies and lightweight spiking layers, trading off representational capacity for lower latency. A recurring theme across these lines is the tension between expressive power—needed to capture complex geometric features—and the strict energy budget that motivates SNNs in the first place. Spiking Mamba Transformer[0] sits at the intersection of these concerns, leveraging selective state-space mechanisms to maintain competitive accuracy while aiming for the event-driven sparsity that distinguishes SNNs from their ANN counterparts.

Claimed Contributions

Spiking Local Offset Attention module

The authors introduce a Spiking Local Offset Attention (SLOA) module that leverages sparse, event-driven spiking computation to capture fine-grained local geometric features in point clouds. This module uses K-Norm and K-Pool for local feature propagation and aggregation, followed by spiking neurons to convert features into binary sequences, thereby reducing energy consumption compared to traditional attention mechanisms.

10 retrieved papers
Spiking Mamba Block for unordered point clouds

The authors design a Spiking Mamba Block (SMB) that integrates the Mamba state-space model with spiking neural networks to achieve global feature integration with linear complexity. The SMB employs a bidirectional scanning strategy and dynamic spiking gating mechanism to handle unordered point clouds efficiently while maintaining low energy consumption.

10 retrieved papers
3DSMT hybrid spiking architecture

The authors propose 3DSMT, a hybrid spiking Mamba-Transformer architecture that integrates the Spiking Local Offset Attention module for local feature extraction with the Spiking Mamba Block for global feature integration. This architecture operates within an energy-efficient spiking neural network framework, balancing high performance with low energy consumption for point cloud analysis tasks.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Spiking Local Offset Attention module

The authors introduce a Spiking Local Offset Attention (SLOA) module that leverages sparse, event-driven spiking computation to capture fine-grained local geometric features in point clouds. This module uses K-Norm and K-Pool for local feature propagation and aggregation, followed by spiking neurons to convert features into binary sequences, thereby reducing energy consumption compared to traditional attention mechanisms.

Contribution

Spiking Mamba Block for unordered point clouds

The authors design a Spiking Mamba Block (SMB) that integrates the Mamba state-space model with spiking neural networks to achieve global feature integration with linear complexity. The SMB employs a bidirectional scanning strategy and dynamic spiking gating mechanism to handle unordered point clouds efficiently while maintaining low energy consumption.

Contribution

3DSMT hybrid spiking architecture

The authors propose 3DSMT, a hybrid spiking Mamba-Transformer architecture that integrates the Spiking Local Offset Attention module for local feature extraction with the Spiking Mamba Block for global feature integration. This architecture operates within an energy-efficient spiking neural network framework, balancing high performance with low energy consumption for point cloud analysis tasks.

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