3DSMT: A Hybrid Spiking Mamba-Transformer for Point Cloud Analysis
Authors: Zhouzhiming
Yong He
Qiaoyun Wu
Chaoxu Mu
Ajmal Saeed Mian
Venue: ICLR 2026 Poster (2026)
Keywords: Point Cloud Analysis
Spiking neural network
Spiking Local Offset Attention
Spiking Mamba Block
Primary area: applications to computer vision, audio, language, and other modalities

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

Published as a conference paper at ICLR 2026
3DSMT: A HYBRID SPIKING FOR POINT CLOUD ANALYSIS
Zhiming Zhou', Yong He Chaoxu Mu Qiaoyun Wu', Ajmal Mian
Annuu University
Tianjin University
Nanjing University of Aeronautics and Astronautics
"The University of Western Australia
wa23301159@stu. ahu. ea, h. yongeehn. edu. on
cxmu ju. edu.cn cn, wuqi aoyuneruaa edu.cn, a ajmal. wal.llmann@uuw edu au
ABSTRACT
The sparse unordered structure of point clouds causes unnecessary computation and energy consumption in deep models. Conventionally, the Transformer archi- tecture is leveraged to model global relationships in point clouds, however, its quadratic complexity restricts scalability. Although the Mamba architecture en- ables efficient global modeling with linear complexity, it lacks natural adaptability to unordered point clouds. Spiking Neural Network (SNN) is an energy- effficiet alternative Artificial Neural Network (ANN), offering 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 hybrid spiking Mamba-Transformer (3DSMT) model for point cloud analysis. 3DSMT integrates a Spiking Local Offset Attention module to efficiently capture fine fine-grained local geometric fea- tures with spiking Mamba block designed for unordered point clouds to achieve global feature integration with linear complexity. Experiments show that 3DSMT achieves state-of-the performance among SNN-based methods in shape clas- sification, few-shot classification, and segmentation tasks, significantly reducing computational energy consumption while also outperforming numerous ANN- based models. Our code is available.
INTRODUCTION
Point cloud analysis essential for enabling machines to perceive and understand complex 3D environments, driving wide range of applications such as autonomous driving, robotics, aug- mented/virtual reality, and metaverse interactions. While significant progress has been made in point cloud processing using Artificial Neural Network (ANN) PPiddy (2005), achieving high curacy often comes at the cost of substantial computational complexity and energy consumption. Energy inefficiency poses major barrier deploying advanced 3D perception systems resource- constrained edge devices, such as drones, mobile robots, and AR/VR headsets, which require real- time processing and have limited power. Consequently, the development of high-performance yet energy-efficient models for point cloud analysis is emerging as a critical and formidable research frontier, essential for advancing practical applications.
Transformer architectures, particularly variants such as Point Transformer Zhao et al. (2021) have demonstrated remarkable success in modeling complex relationships in point clouds, further advanc- ing the capabilities of ANN- methods in this domain. Their self-attention mechanism excels capturing global dependencies,
the standard dot product operation used in these architectures has quadratic computational complexity (O(N2) with respect to the number of points (N). This high computational cost severely limits its applicability to large-scale point clouds, despite effec- tiveness in global modeling. To address this challenge, localized attention mechanisms have been adopted to reduce energy consumption and improve efficiency point cloud processing.
"Corresoonding author: Yong He (h www.yonn..geeeeeoo www.gehhuu.uu...mm edu.en)

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Recently, state-space model (SSM) such as Mamba Gu & Dao 2023 have emerged an alterna- tive solution offering linear computational complexity (O(N)) and enabling efficient global context integration. Compared to Transformers, Mamba consumes less energy and yet captures long- Long-range dependencies across large scale point clouds.
However, with these advancements, the computational and energy demands of point cloud anal- ysis remain high, especially for real-time applications on address these challenges, Spiking Neural Network Maass (197), inspired by the brain's information processing, have emerged as a disruptive paradigm, already demonstrating significant progress in 2D image analysis Zhou et al. (2023b]a) a). SNN communicate via sparse. event-driven binary spikes and addition operations to achieve ultra-low computational energy consump-
devises. To further
leverage energy-efficiee tion on neuromorphic hardware Roy et al. (2019). Their event-driven nature aligns perfectly with the sparse distribution of point clouds, while their temporal dynamics provide a natural framework for sequential feature reasoning, making them highly promising for sustainable 3D perception.
se
To combine the energy efficiency of SNN with powerful local and global modeling capabilities of for point cloud analysis
Transformer and Mamba, we propose a hybrid spiking Mamba-TransSore (coined 3DSMT). The core insight is to jointly integrate attention based local modeling and SSM- based global modeling within an spiking framework. Our SNN-based 3DSMT model is applicable to wide range of point cloud analysis tasks. Our contributions summarized follows:
We propose Spiking Local Offset Attention that captures fine-grained geometric structures through sparse, event-driven computation, reducing computational and energy costs.
We introduce a novel Spiking Mamba Block that achieves efficient global feature integra- tion with linear complexity and low energy consumption, leveraging Mamba's strengths in processing large-scale sequences.
We present 3DSMT, hybrid spiking architecture that combines the local feature extraction of Spiking Local Offset Attention and the global feature integration of the Spiking Mamba Block, within an energy energy-efficient spiking paradigm, for point clouds analysis.
Our method achieves state-of-the-ar performance of 92.0%, 92. % and 90.6% Overall Accuracy (OA) on the three variants (i.e., T5.RS.S OBJ_BG. OBJ_ONLY) of ScanObjectNN Uy et al. (2019). Additionally, achieved 95.2% OA on ModelNet40 Wu et al. (2015) for classification with reduced energy consumption. In the few-shot classification setting on ModelNet40, achieves 92.8% and 96.2% OA in 5-way 10-shot and 20-shot settings, respectively, and 87.2% and 92.1% in 10-way 10-shot and 20-shot settings. Furthermore, it achieves 85.1% instance mean In- tersection over Union (Ins.mloU) on ShapeNetPart Yi et al. (2016) for part segmentation which the highest among SNN-based methods. Notably, our method also outperforms the majority of ANN-based methods while significantly reducing computation and energy consumption.
method
2 RELATED WORK
ANN-based Point Cloud Analysis. Deep neural network architectures for point cloud data based on traditional ANN can be roughly divided into projection-based voxel-based, and point-based meth- ods. Projection-based methods simplify 3D tasks by converting them into 2D image problems, often leading to information loss. Voxel-based methods transform point clouds into voxel grids and apply 3D convolutions, but the computational cost grows cubically with resolution. PointNet Qi et al. (2017a). a pioneering point-based method, uses MLP to extract global information from unordered point sets. PointNet++ Qi al. (2017)) further introduces hierarchical feature learning capture local geometric structures, since becoming the cornerstone of modern point cloud analysis.
With the development of deep learning, point cloud analysis has shifted from MLP-based meth- ods to transformer-based approaches. Point Transformer Zhao et al. (202T), which fuses local and global features, has become the state-of-the-art backbone for point cloud analysis. However, its quadratic complexity increases computational costs, posing challenges for long-sequence point clouds. Recently, Mamba with linear complexity and powerful modeling for capturing long-range dependencies- has emerged more efficient alternative. PointMamba Liang al. (2024) first introduces the Mamba architecture into point cloud processing, significantly improving efficiency while maintaining high accuracy. Nevertheless, both methods constrained by the inherent energy
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inefficiency of traditional artificial neural network, which remains urgent problem solve in the field of point cloud analysis.
SNN-based Point Cloud Analysis. The integration of spiking neural network with point cloud processing is a novel and energy-efficient approach, but remains in its early stages of development. Spiking PointNet Ren et al. (024) proposes a 'less training but more learning' framework based on PointNet, being the first to apply SNN to point clouds. P2SResLnet Wu et al. (2024a) combines spik- ing neurons with traditional point convolutions to construct a point-to-spike residual classification network. SPT Wu et al. (2025a) designs a queue-driven sampling direct encoding method for point clouds, building the first Transformer-baseee point cloud classification framework. SPM Wu et al. (2025b) the first spike point cloud analysis framework based on Spike Mamba. It not only brings spike-based pre-training into the point cloud field, but also uses a two-branch time-flippigg strategy to fit spike point cloud processing. However, our proposed 3DSMT effectively integrates Mamba with point clouds through a strategy that combines two-branch horizontal flipping and channel flip- ping. Current, the above SNN models still have a significant accuracy performance gap compared with ANN models, and further improving the accuracy of SNN based point cloud analysis models remains an open challenge.
Current research on integrating SNN with point cloud data faces significant accuracy gaps. The core issues lie in insufficient feature representation capabilities, immature training mechanisms, and a trade-off that often sacrifices accuracy excessively for lower energy consumption. To bridge this gap and fully harness the potential of spiking neural network in point cloud processing, it is crucial to develop an architecture that balances accuracy and energy efficiency. Our proposed 3DSMT addresses these challenges by leveraging Integrate-and- excel in energy-sensitive tasks due to their low memory and energy requirements. By adhering strictly to direct training with surrogate gradients. 3DSMT avoids the additional temporal overhead associated with ANN-SNN conversion, ensuring low energy consumption. Furthermore, 3DSMT effectively combines the pegfooeeformance characteristics of Transformer and Mamba models in point cloud processing, achieving an optimal mnergy-accuragy balance.
(IF) neurons [Bulsara et al. (996)). which
METHOD
We propose hybrid spiking Namba-Transfomen network (3DSMT) that delivers high performance at low energy consumption. 3DSMT is a spiking neural network specifically designed for point cloud analysis. Below, we outline the overall 3DSMT framework, followed by a comprehensive exploration of its critical design components.
3.1 3DSMT OVERVIEW
As illustrated in the top of Figure 1 the 3DSMT architecture composes three key components: Spiking Patch Embedding. Spiking Hybrid Block and Prediction Head. The following sections will delve into the details of each components.
Spiking Patch Embedding. Given input point cloud P E RNN33 containing N points, we first use the farthest point sampling (FPS) to select I. center points Pa E LR3 Then, for each center point Pi we construct a local patch x Fx3 using K-nearest neighborhood (KNN). Note that spiking neurons endow the network with spatio-temporal characteristics, and T is a key hyperparameter for time delay. x3 is copied for each time step t E [0, T) as input E R R to the Spiking Patch Embedding (SPE). As shown in Figure [] SPE can be formulated as,
U = MLP(SN(MLP(E)) G = MaxPool(U"), U' = MLP((N(Concat(U, (U)) (1,2,3)
here the membrane potential U is the preliminarily learned deeper semantic information, G is the output of MaxPool on U to capture the local context of each token, and U' is the output obtained by processing the concatenation of U and G through the SN layer and MLP. The MLP in this context implemented using a ID convolution layer (Conv ID) and a Batch Normalization layer (BN). The SN layer, on the other hand, consit of a Spiking Neuron Layer.
Spiking Hybrid Block. The acquired patch embeddings regarded as token sequences in Trans- former architecture, analogous to ViT Dosovitskiy et al. (2020). Specifically, we introduce a learn- able [CI.S] token aggregate information of the entire sequence. Each patch xp Lx is
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404
T22th
Park
Spike Neuron Layer
(b)
PPS KNN
Spiking Embedding
(a)
Spiling
Hybrid
Spikking
Hybrid
Part Sigg
Tosk
Concal
Subtract
Block
Block
Fewshoot
Addition
Tisl
12/h
Classif.
Muhtiply
L+SSM
SPE
C-SSM
SNEB
L.
(b~1) Spiking Mamba Block
(b-2) Spiking Position Encoding
Maspooll
SLOA
$1.04
LRS
Conv-ID
S
Matrix
Dut-Product
ConvIDD
SN
SPF
LOS
Q.KT.V)
S
(4) Spiking Patch Emhodding
(b) Spikinn
(b-3) Spiking Local Offict Artention Block
Hybrid Block
Figure 1: 3DSMT overview. The model comprises Spiking Patch Embedding (SPE) module, sequence of Spiking Hybrid Blocks (SHBs), and a task-specific head. The output the (i-I)-th SHB serves the input to the i-th SHB. (a) The SPE module first maps low-dimensional point coordinates into a ligh-dimensional feature space, which serves as the input to the first SHB. (b) Each SHB integrates Spiking Local Offset Attention (SLOA) block, Spiking Mamba Block (SMB), and a Spiking Position Encoding (SPE) module to capture local and global features.
projected into a C-dimensional feature vector Fx(x))) E RLXC serving as its initial patch embed- ding. After incorporating position encoding, the [CLS] token is vertically stacked with all point cloud patch features form a geometric structure information sequence of shape (L+ 1) (con- sisting of L patch features plus learnable [CLS] token). This sequence is subsequently fed into the Spiking Hybrid Block (SHB) for high-level feature learning. As illustrated at the bottom of Figure SHB comprises Spiking Local Offset Attention (SLOA) and Spiking Mamba Block (SMB), which are used to extract local and global features, respectively, Each module preceded by a Layer Norm layer (LN) and employs residual connections. Specifically, it can be described as,
O [Xassi (p(())) F: + Spos:
O SLOA(LN(O i-1 Spos)) 0
(5)
O SMM(LN + +0", 11....
(6)
where O' denotes the output of each SHB layer, Xate E RRICC is the learnable [CLS] token, and LN significs the LayerNorm operation. practice, we stack M 12 SHBs, and each SHB incorporates Spiking Position Encoding Spos E ((++I)x enhance the model's 's spatial awareness.
Prediction Head. The prediction head includes a classification head and segmentation head. Classification Head: Stacked linear layers that map high-level SHB features to class predictions. It excels both standard shape classification and few-shot learning with limited labels. Segmentation Head: Inspired by PointBERT Yu et al. (2022), it combines features from the 4th, 8th, and 12th SHB layers (like PointNet++) and outputs point-wise part probabilities for fine-grained segmentation.
3.2 SPIKING LOCAL OFFSET ATTENTION
Local geometric features crucial for point cloud feature learning. To exploit these features, Mamba3D Han et al. (024) use K-Norm to propagate local features from the central point to ad- jacent points, while K PoPo effectively performs local feature aggregation. Inspired by this, propose a Spiking Local Offset Attention (SLOA). As shown in Figure given the input S, we first use K-Norm and K-Pool for local feature propagation and aggregation obtain S,. Then, the SN
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layer converts the local features into spiking sequence }0.1} with apatio-temponn information, fol- lowed by channel alignment through MLP. The features are then converted into a spiking sequence S2 via the SN layer again. Finally, obtain query Q, key K, and value V matrics through linear transformations and feed them into distinct spiking neurons. We abbreviate the sequential operation of Linear, Batch Normalization, and Spiking Layer as LBS, which can be expressed as,
S = K-Pool K K-Norm( S)),
S2 = SN(MLP(SN(S.)))
(7,8)
Q (S2),,),
K LBS(S)).
(9,10)
= LSB())),
A = Q K" VV
(11,12)
where Q. K, V E RTXNXD are represented in spiking form. Due to the inherent sparsity and non-negativity of spiking sequences, the calculation of the attention matrix A can be directly ac- complished using simple logical AND operations and accumulation (AC) operations. This avoids the energy-intensive floating-por point multiply multiply-accumulate (MAC) operations caused by the softmax function in traditional ANN, dramatically reducing computational energy consumption. Subse- quently, the attention layer computes the offset between the attention features and the input feature S2 through element-wise subtraction. After processing the offset through the SN and MIP, it is added to the input feature S2 to obtain the output feature S3. expressed as,
S3 MIP(SN(S22 SN(A))) + S2-
(13)
By introducing this spiking offset attention mechanism, SLOA can more explicitly highlight the differences in features within local regions of point clouds, strengthen the ability to capture subtle structural changes, effectively improve the fine-grained local features representation and overall analysis performance.
3.3 SPIKING MAMBA BLOCK
VIM Zhu et al. (2024) leverages its efficiency in long-sequence modeling to propose bidirectional state space model (bi-ssm) for visual feature adaptation. However, unlike the structured grids of images, point clouds are unordered and irregular, making the learning of sequence order lead to un- stable pseudo-sequential dependencies. Building upon this, Mamba3D introduces Mamba archi- tecture with a bi-ssm strategy to enhance the effectiveness of point cloud data processing. Inspired by this, we propose the Spiking Mamba Block (SMB), which integrates the global efficient model- ing of Mamba with the energy-saving advantages of Spiking Neural Network. SMB first converts the input feature Z into binary spiking sequence Zı via the SN layer, achieving sparse synap- tic accumulation. reducing feature space redundancy. SMB consists of an SSM branch and Gate branch: for the SSM branch, the spiking sequence Z1 encoded using Linear and SN layers to obtain a new spiking sequence 7s. Subsequently. features Zr, and Zc are obtained via bidirec- tional scanning strategy to capture bidirectional feature dependencies. The Gate branch produces a sparse spiking gating matrix ZG via Linear and SN layers, which performs feature selection through Hadamard product with Z,, and Zc, achieving feature selection and filtering. The product matrices are summed to obtain Z2. which is finally processed by the SN layer and Linear layer to yield the SMB output Zz. The entire process of SMB can be expressed as,
Zi SN(Z), Zs SN(Lin(Z1)), Za (N((Ln(Z))) (21)), Zo = C-SSM(Conv id(Zs)), Z, (Zr. s ZG) (Zo & Za), Za Lin(SN(Zz))) (2));
(14,15,16)
2, L+SSM(Conv
(17,18)
(19,20)
here L+SSM and C-SSM denote the scanning strategies proposed by the original Mamba Gu & Dao (2023) and Mamba3D Han et al. (2024), respectively. Lin represents the Linear layer, & represents the Hadamard product. The SMB significanlly reduces feature redundancy through spiking sequence sparsification while employing a non-causal Conv ID design to eliminate temporal artifacts. By incorporating bidirectional scanning, SMB enhances model capability for complex spatial structures, along with a dynamic spiking gating mechanism that selectively activates salient features while suppressing noise/redundancy. In all, This architecture achieves remarkable reduction in energy consumption while substantially improving both the adaptability and representational capacity for point cloud processing tasks.
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ScanObjectNN OBI BG
ModelNet40
Type
Methods
Year
FLOPs
ISU RS
OBT ONLY OAT
OA
Energy
OAT
OAt
PointNeXt
22 22'NIPS
3.6
87.7
94.0
16.6
BERT
22'CVPR
87.4
88.1
22.1
Point-M2AE
22'NIPS
3.6
86.4
91.2
88.8
94.0
16.6
PTv2
22'NIPS
17.1
93.7
78.7
PointNN
23'CVPR
1.0
87.1
93.8
4.6
ANN
PointMamba
24'NIPS
3.6
82.5
88.3
87.8
92.4
16.6
PCM
AAAI
45.0
88.1
207.0
SIM
2V'CVPR
3.6
87.3
92.3
91.4
92.7
16.6
Spike PointNet
23'CCVV
0.1
69.2
88.6
0.1
SpikePointNet
24'NIPS
0.4
64.1
72.2
76.4
88.2
0.4
Net
AAAI
3.3
81.0
78.6
80.2
88.7
3.0
SNN
SPT SPM
AAAI
14.0
78.0
82.8
83.4
91.4
13.3
25'CCCV
84.2
90.2
89.5
92.3
5.4
3DSMT w/o vot.
1.3
90.4 (+6.2)
90.8 (+0.6)
89.7 (+0.2)
94.7 (+2.4)
4.3
3DSMT w/ vot.
92.0 (+7.8)
92.1 (+1.9)
90.6 (+1.1)
95.2 (+2.9)
4.3
Table I: Classification results on ModelNet40 and ScanObjectNN. '-' denotes that the model did not provide results, The units of OA, Energy and FLOPs percentage (%), millijoule (mJ) and Gigabyte (G), respectively. "w/o vor' denotes the method without voting strategy, while 'w vor' indicates testing with voting strategy applied. Among the SNN-based methods, the best results presented in bold, and the second-best results are underlined.
3.4 SPIKING POSITION ENCODING
To process the positional information of point clouds in spiking neural network, we propose learn- able point cloud position encoding method. By alternately stacking Spiking Neuron (SN) layers and Multi-Layer Perceptron (MLP), this method achieves ppatio-temporal feature encoding for the spatial structure of 3D point clouds. For each point p (æ, y. 2) E R in the input point cloud P E IR Nx3 the raw coordinates are first initialized with -patio-temporal features using SN. This layer leverages the membrane potential dynamics of neurons to capture time-dependent features of the initial spatial positions. Subsequently, an MLP performs feature transformation to enhance the representation of geometric features, outputting the final position encoding.
EXPERIMENTS
We evaluate the performance of 3DSMT classification network on ModelNet40 Wu et al. (2015) and the three main variants (PB T50 RS, OBJ_B,, OBJ (BL_Y of ScanObjectNN et 2019 dataset. We evaluate the 3DSMT segmentation network on the ShapeNetPart Yi et al. (2016) dataset for part segmentation. All experiments are conducted on server equipped with an AMD EPYC 7571 2.1 GHz 32-core processor and NVIDIA RTX 3090 GPU with 24GB memory. Our im- plementation is based on PyTorch SpikingJelly Fang et (2033). Detailed descriptions of the datasets and experimental settings are in the Appendix.
4.1 COMPARISON WITH STATE-OF-THE-ARE METHODS
We conduct a rigorous performance comparison of 3DSMT with existing ANN- and SNN-based point cloud analysis methods. For evaluation, we adopt overall accuracy (OA), Energy, FLOPS and Parameters (Para.) as metrics for classification, OA for few-shot classification, as well as category mloU (Cat. mIoU) and Instance mloU (Ins. mloU) for part segmentation.
Classification on ModelNet40. As shown in Table ! our 3DSMT achieves an accuracy of 94.7% when trained from scratch, and reaches a top-1 accuracy 95.2% after optimization by the vot- ing strategy Liu et al.] (2019)(a common post-processing technique in point cloud classification, as in Mamba3D Han et al. (2024)). Compared with the current state-of-the-art SNN-based method SPM Wu et al. (2025b), 3DSMT improves accuracy by 2.9% while also reducing energy consump- tion by 1.1 m.J. Compared with ANN-based methods, 3DSMT outperforms the single-Man Mam-a- based method PCM Zhang al. 2025) (93.4% OA) by 1.8%, and reduces energy consumption by at least 48 times. Furthermore, 3SDMT surpasses the single-Transforme Transformer- based method PTv2 Wu et al. 2022
(93.7% OA) by 1.5% and reduces energy consumption by least 18 times. These results

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Published as a conference paper at ICLR 2026
5-way
10-way
Methods
Year
Type
10-shol
20-shol
10-shot
20-shol
Poin
17'NPSS
ANN
38,5# 3.6
42.4 2.9
23.1 4,8
18.8 5.2
Transformer
17'NIPS
ANN
87.81 5.2
93.31 4.3
84.61 5.5
89.41 6.3
DGCNN
21'NIPS
ANN
31.6 2.8
40.8+ 4.6
19.9 2.1
16,9# 1.5
Mamba3D
2M'MM
ANN
92.61 3.7
2.
88.14
93.11 3.6
11-Emba3D
25 25'arXiv
ANN
90.5# 3.8
96.0+
86.3+
92.0= 3.4
SpikePointNet
24'SIPS
SNN
52.3+ 3.2
57.1 3.8
46.2 2.6
34.1 4.2
3DSMT
SNN
92.8: 4.3
96.2 3.9
87.2 5.7
92.1 6.1
Table 2: Few-shot Classification results on ModelNet40.
highlight the superior performance of the 3DSMT hybrid architecture, and demonstrate the signifi- cant potential of SNN models for point cloud analysis.
Classification ScanObjectNN, We evaluate the performance of 3DSMT three variants of the ScanObjectNN, As shown in Table [ 3DSMT achieves the best accuracy performance with 90.4%, 90.8%, 89.7% OA on PB. PB_550KK RS, OBJ LGG OBJ JOLY variants respectively, when trained from scratch on each variant. After applying the voting strategy, the OA improves to 92.0%, 92. 1,, and 90.6%, surpassing the second best SNN-based method SPM Wu et al. (2025b) by 7.8%. 1.9%, and 1.1%, respectively with fewer FLOPs (1.3G). Notably, our method demonstrates outstanding accu- racy performance on the most challenging variant PB T50 RS. Compared to ANN- methods, 3DSMT performs comparably to SIM Bahri et al. (2025) while requiring fewer FLOPs. This not only demonstrates the effectiveness of our hybrid Transformer-Mante design but also highlights the performance gain of SNN's unique temporal feature extraction capability.
Few-shot Classification on ModelNet40. We evaluate the few-shot learning ability of 3DSMT on ModelNet40. Following the few-shot learning setup of paper (hharma & Kaul (2020), conduct experiments with n-way m-shot configurations, where n E {5, 10} and m E { 10, 20). The results reported in Table When training from scratch, the 3DSMT achieves OA of 92.8%, 96.2%, 87.2% and 92.1 % under the four experimental settings, respectively, outperforming the SNN-based method SpikePointNet by large margin. Moreover, 3DSMT outperforms series of ANN-based baseline and get the similar performance with Mamba3D Han et al. (2024). This verifies the strong transfer ability of SNN-based 3DSMT to downstream tasks under limited data conditions.
Part Segmentation on ShapeNetPart. We applied 3DSMT to point cloud part segmentation on the ShapeNetPart dataset. 3 that our SNN-
Methods
Year
Type CatmIoU Ins.mloU
PointNet++
17'NIPS ANN
81.9
85.1
based 3DSMT still performs well, achieving 82.7%
PointMLP
22'ICLR ANN
84.6
86.1
APES
23'CVPR ANN
83.1
85.6
Cat.mloU and 85.1% Ins.mloU-0.44 and 0.3% higher than the prior SOTA method SPM Wu et al.) (2025)), respectively. The performance improve- ment in segmentation smaller than in classifica- tion mainly due to dataset differences: ShapeNetPart is noise-free, fully-formed synthetic data (casy for feature extraction), while ScanObjectNN (used for classification) is real-world data with noise and oc- clusions, which requires more sophisticated feature extraction techniques. This highlights 3DSMT's strength in real-world settings. Besides, most ANN methods show little performance difference on ShapeNetParl (OA mostly 85%-87%) indicating lim- ited distinguishability and potential drawbacks of the dataset. Even so, 3DSMT outperforms existing SNN methods and classic ANN baselines like PointNet++ + al. (2017)).
PointMamba
24'NIPS ANN
84.4
86.0
PCM
2A'A ANN
85.6
87.1
SIM
CVPR ANN
84.1
85.9
SPT
25'AAAI
SNN
81.3
82.9
SPM
25'CCCV
SNN
82.3
84.8
3DSMT
SNN
82.7(+0.4)
85.I(+0.3)
Table The Part Segmentation results on ShapeNetPart.
Semantic Segmentation on S3DIS. We conduct semantic segmentation experiments on the A3mi&Armeni et al.) (2016) dataset, comparing our proposed SNN-based method 3DSMT with ANN-based methods and SNN-based methods in terms of mloU, per-category IoU, and energy consumption. As shown Table 3DSMT achieves the best performance among SNN methods with a mloU of 70.2%, outperforming E-3DSNN (67. 66.44% by 2.7 percentage points. meanwhile, it maintains the lowest energy consumption 11.4 1.4 mJ, which is more emeryy-efficien than E-3DSNN (14.4 mJ). Among all compared methods, ANN-based PTv3 achieves the highest mloU (73.6%) but consumes much more energy (687.7 mJ), while 3DSMT balances segmentation accuracy and energy efficiency effectively for SNN-based semantic segmentation tasks.
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Methed
Type
Type
mloU
ccil.
floor
wall
colu.
wind.
door
table
chair
sofa
book.
board
clut.
Energy
PointNet
17'CVPR ANN
41.1
88.8
97.3
69.8
3.9
46.3
10.8
59.0
52.6
5.9
40.3
26.4
33.2
5.5
PointNete
17'NIPS
ANN
53.5
89.4
97.7
75.4
1.8
58.3
19.5
79.0
69.2
59.1
46.2
58.7
41.6
5.5
PoinCCNN
18'SPS
ANN
57.3
92.3
98.2
79.4
17.6
22.8
62.1
74.4
80.6
31.7
66.7
62.1
56.7
324.5
PointNeXt
22'NIPS ANN
70.5
94.2
98.5
84.4
37.7
59.3
74,0
83.1
91.6
77.4
71.2
78.8
60.6
PCM
ASAA
ANN
63.4
93.3
96.7
80.6
35.9
57.7
60.0
74.0
87.6
50.1
69.4
63.5
55.9
Po
25 ASAA ANN
70.5
38.6
88.2
78.2
PTvl
21'CCV
ANN
70.4
94.0
98.5
86,3
38.0
63.4
74.3
89.1
82.4
74.3
80.2
76.0
59,3
76.8
PTV2
22'NIPS
ANN
71,6
93.0
98.1
56.7
48.0
62.4
76.1
88.3
87.6
77.1
79.2
77.5
59.8
400.1
PIV3
24'CPR
ANN
73.6
92.4
98.3
86.6
55.8
63.7
77.1
83.8
93.3
79.1
79.4
85.4
61.7
687.7
B-ADSNN
25'AAAI
SNN
67.4
95.3
98.5
82.3
28.0
55.8
71.5
81.2
89.8
69.2
76.4
67.0
61.6
14.4
3DSMT(UU))
SNN
70.2
88.9
94.2
82.5
46.8
02.0
74.4
85.3
87.3
77.3
76.9
77.6
59.8
11.4
Table 4: Semantic Segmentation Results on S3DIS Dataset.
The unit of energy consumption mJ.
Scene Segmentation on Semantic KITTI We compare our SNN-based methods 3DSMT with ANN-based methods and SNN-based methods on SemanticKITTI [ehlley et al. (2019), with the metric being mIoU (Val/Test). As shown in the table5] 3DSMT outperforms E-3DSNN among SNN, achieving 68.1% (Val mloU) and 71.3% (Test mloU), which are 4.9 and 1.9 percentage points higher respectively. While ANN method PTv3 reaches the highest Test mloU (75.5%), 3DSMT as a point cloud SNN shows competitive performance.
Method
Type
Input
Val
Test
Training
Inference
Methods
SPVNAS
ANN
point
64.7
66.4
Latency
Memory
Latency
Memory
Cylinder3D
ANN
point
64.3
67.8
SPT-512
326ms
9.7G
191ms
5.2G
PTv2
ANN
point
70.3
72.6
SPT-768
385ms
12.5G
201
7.3G
PTv3
ANN
point
72.3
SPT-1024
431ms
15.2G
227ms
9.5G
E-3DSNN
SNN
voxel
63.2
69.4
Mamba3D
433ms
14.9G
256ms
7.8G
3DSMT((ur)
SNN
point
68.1
71.3
3DSMT(our)
298ms
10.1G
142ms
4.6G
Table 5: Scene segmentation results on Semantic KITTI Dataset.
Table 6: Model Efficiency on ModelNet40 (Latency, Memory).
4.2 ABLATION STUDIES
To thoroughly investigate the performance for 3DSMT, we conducted a series of ablation experi- ments on the ModelNet40 or ScanObjectNN, examining various factors such as hybrid architecture, threshold, time steps, neighbor scale, token, ordering and bidirectional Strategies. We further con- ducted ablation studies on spiking neurons, point cloud size, Spiking Hybrid Block number, 3DSMT (without spiking), and efficiency the Appendix. In addition, we provide details on energy con- sumption estimation, hardware adaptability. and further visualization results in the Appendix.
Analysis on Efficiency. We evaluate model efficiency on the ModelNet40 dataset based on two simple metrics: Latency and Memory. Table T shows that the Latency and Memory of ANN-based Mamba3D are both higher than those of SNN models. For the rics rise significantly as the number of sampling points increases. Our proposed SNN-based 3DSMT has obvious efficiency advantages. It achieves the best performance among all compared methods with 298ms training Latency, 10.1G training Memory, 142ms inference Latency, and 4.6G inference Memory. This result shows that 3DSMT can effectively reduce the time and hardware resource costs of training and inference, and achieve good balance between efficiency and performance.
SPT series, the two met-
Analysis on Hybrid Architecture. We designed a set of control experiments to test the contri- butions of the hybrid strategy (Transformer and Mamba modules) and SSM branch (unidirectional SSM vs. bidirectional SSM) in the hybrid architecture to accuracy and energy efficiency. i) As shown in 7 the ANN-based Full model achieves an OA of 94.9% on ModelNet40, but its en- ergy consumption is as high as 36.3 mJ, which is significantly higher than all SNN models. Among SNN models, the No-MT model (without Transformer and Mamba) serves as the basic baseline with the lowest accuracy; although its energy consumption is only 3.3 mJ, its performance is in- sufficient, while the Only-T model (Transformer only) shows improved accuracy compared to the No-MT model, and the Only-MU (unidirectional SSM only) and Only-MB (bidirectional SSM only) models (Mamba only) achieve further optimized performance, proving the enhancement effect of Mamba modules, and the hybrid Full-MUT and Full-MBT models reach the highest accuracy to
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Hybrid Stratogy
SSM Branch
SeanObjectNN
ModelNet40
Method
Type
Transformer
Mamba
Unidirection
Bidirection
OAT(BG)
OATIONLY)
OAT(RS)
OAT
Energy
Fall
ANN
91.2
90.4
91.0
94.9
36.3
No-MT
SNN
X
x
x
85.5
84,0
86.2
92.1
3.3
Only-T
SNN
K
88.0
$6.9
87.7
91.8
4.1
Only-MU
89.5
Only-MB
SNN
X
89.6
88.4
89.2
94.0
4,0
SNN
90.2
89.3
90.1
94.2
4,3
Full MBT
SNN
8
90.8
89.7
90.4
94.7
4,3
Table 7: Analysis on hybrid spiking Mamba- Transforrnnffrerrr architecture. BG + OBJ_BG, ONLY - OBJ ONLY, RS PB T50 RS.
k
OA (%)
mAce (%)
L
OA (%)
mAcc (%)
93.6
90.3
89.6
93.7
90.7
93.4
90.2
4
94.7
91.8
128
94.7
91.8
6
94.0
91.1
256
94.0
90.9
Table 8: Effect in SLOA on ModelNet40
Table 9: Effect of tokens on ModelNet40
date daon- them, Full-MBT (hybrid strategy bidirectional SSM) performs the best among SNN models, with OA values of 89.7%, 90.4%, and 90.8% on various ScanObjectNN variants and 94. 7% on ModelNet40. ii) The impact of SSM branch configuration is also significant: for Mamba-only models, Only-MB (bidirectional SSM) more accurate than Only-MU (unidirectional SSM) (e.g., ModelNet40 OA rises from 93.8% to 94.0%), and for hybrid strategy models, Full-MBT (bidi- rectional SSM) has higher accuracy than Full-MUT (unidirectional SSM) (e.g., ModelNet40 OA increases from 94.2% to 94.7%); notably, bidirectional SSM does not add extra energy consump- tion, indicating that it can capture more comprehensive information and improve model performance without sacrificing energy efficiency
Analysis on SNN Threshold and Timestep. spiking neural network (SNN), the threshold de- cides how easy for neurons to activate, and the timestep shows the network's ability col- lect temporal information. Both are key parame-
94.6
93.4
93.8
94.1
93.9
94.2
Threshold
93.6
93.8
94.3
94.0
ters unique to SNN. We did multiple sets of ex- periments using different combinations of thresh- olds (0.5, 1.0, 1.5, 2.0) and timesteps (1, 2,
94.0
93.8
1.0
93.8
94.3
93.6
4). The results are shown in Figure 2 When threshold
fixed, increasing the timestep can make the SNN better at collecting tempo- ral features and capturing point cloud informa- tion. However, lengthy timesteps (i.e., beyond 3) may bring in unnecessary information and de- grade performance. When the timestep is the same, a too-low threshold brings in noise, and a too-high stops useful features from working.
93.4
93.1
93.4
93.8
93.6
-93.2
TimeStep
Figure Analysis on threshold-timesepp com- binations for classification accuracy.
So the model's performance first goes up and then drops as the threshold decreases. After optimizing the experimental setting, the best parameters for the SNN are TimeStep=3 and Threshold=1.0. With this setup, the model reaches the highest overall accuracy (OA) of 94.7%.
Effect of Neighbor Scale and Token. We analyzed the 3DSMT model performance under different parameters. In table 8 the scale of the local neighborhood was adjusted by varying the parameter the SLOA module. Experiments show that as & increases from to 4, the OA and Mean Class Accuracy (mAcc) of the model gradually improve, reaching peak values of 94.7% and 91.8% at = However, when exceeds 4, redundant background information introduced. leading to performance degradation. Table 9 demonstrates that as the token sequence length L increases, both OA and mAce first rise and then fall, with optimal performance achieved at = 128, beyond which accuracy reduces due to repeated feature extraction.

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Published as a conference paper at ICLR 2026
Ordering Strategies
OA (%)
mAce (%)
Bidirectional Strategy
OÅ (%)
Shuffte All
92.9
90.0
Unidirection-SSM
94.2
90.9
Shuftle SSM
93.2
90.4
L-SSM T-SSM
94.3
91.1
Z-order
93.8
90.9
C-SSM T-SSM
94.3
91.4
No Order
94.7
91.8
I-SSM C-SSM
94.7
91.8
Table 10: Effect of different ordering strategies on the ModelNet40 dataset.
Table 11: Effect of different bidirectional strat- egy on the ModelNet40 dataset.
23.75
21.0
13.25
13/00
Kimbbe Eoe Rate
2110
12.90
12.25
22.50
Ours
11.75
23.0
10
Airplane
Car
Motorbike
Chair
Block Index
Figure 3: The firing rates of Q, K, V and Mini Mend-
Figure The Part segmentation on ShapeNetPart, Top line is ground truth and bottom line is our prediction.
Effeet of Ordering and Bidirectional Strategies. Consistent with Mamba3D's experiments, we first studied ordering strategies and found that 3DSMT still achieves optimal performance without any ordering strategy. Relevant results are Table IO indicating 3DSMT can effectively extract features from unordered point clouds. Based on this, we further evaluated the impact of various scan- ning strategies on point cloud classification accuracy in Table [1 ablation experiments on L-SSM (original Mamba), channel-fliped C-SSM (Mamba3D) and time-ffipped T-SSM (SPM) showed that among unidirectional SSM strategies, Mamba3D's C-SSM (for unordered point clouds) had the highest accuracy. Moreover, bidirectional SSM maintained higher classification accuracy than unidirectional SSM, fully supporting the effectiveness of our proposed SMB module. Among bidi- rectional SSM strategies, the L-SSM + C-SSM combination achieved the optimal 94.7% 7% accuracy. Hence, we adopt this bidirectional scanning strategy.
4.3 FURTHER ANALYSIS
Spike Firing Rate refers the frequency which neuron generates spikes a period of time, used to characterize the average activity level of the neuron (typically measured by the number of spikes per unit time), which can quantify the activation degree of the neuron. As shown in Figure we recorded the firing rates of Q, K, and V in the SLOA module of 3DSMT and the firing rates the spiking layers in the initial input and final output layers of the SMB module. The extremely low firing rate leads to the sparse computation characteristics of 3DSMT.
Spike Firing Rate.
Visualization. We visualize the part segmentation results of 3DSMT and the Ground Truth in Figure 4 The visualization results of 3DSMT are highly consistent with the Ground Truth, with only slight differences in fine structures such as airplane tail contours and car edges, maintaining high overall segmentation quality. While ensuring the accuracy of 3D point cloud segmentation tasks, 3DSMT also provides solution with significant energy efficiency advantages.
5 CONCLUSION
We leverage the event-driven ultra-low power consumption characteristics of spiking neural network to propose an novel hybrid architecture, 3DSMT. To address the energy consumption in processing unordered point clouds, 3DSMT hybridizes the Spiking Local Offset Attention for capturing local geometric details and the Spiking Mamba Block for linear-complexity global feature fusion, achiev- ing an optimal balance between accuracy and efficiency. Extensive experiments show that 3DSMT outperforms existing SNN methods in point cloud shape classification, few-shot classification, and part segmentation tasks. It not only reduces energy consumption but also surpasses many traditional ANN-based models. This paper provides new insights for the development of high-performance and low-energy point cloud analysis. the future, we will further explore the application potential of SNN in complex point cloud tasks such object detection and scene understanding.
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ACKNOWLEDGMENTS
This research was partially supported by the Australian Research Council Discovery Project (DP240101926) and the National Natural Science Foundation of China (Nos. 62206001,52205531, U23B20105, 52575580).
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A SPIKING NEURON MODEL
Spiking neuron is the fundamental unit of SNN, we choose Integrate-and-Fire (IF) Bulsara et al. (1996) model as the spiking neuron in the proposed 3DSMT. The dynamics IF neuron can be formulated as follows:
U, Vi-1 4
S O Vih
(2)
Vi =u St) + Vesett
(3)
Where Vz-1 represents the membrane potential the neuron at time step 1, which undergoes
a spike trigger evaluation. I, represents the input current at time step I. denotes the membrane potential of the neuron after incorporating neuron dynamics at time step t. In Equation (2), (.))
symbolizes the spike trigger evaluation at time step f. When the membranc potential ue exceeds the firing threshold Via the neuron emits a spike, setting S, = 1; otherwise, S, = 0. In Equation (3), V2 represents the membrane potential of the neuron at time step after the spike evaluation. If no spike is produced, V, is equivalent to Uy; otherwise, is reset to the potential Vreset-
B EXPLANATION OF THE PERMUTATION INVARIANCE OF THE SLOA
The Spiking Local Offset Attention (SLOA) module in 3DSMT ensures permutation invariance in the local feature extraction process through three layers of strictly symmetric operation designs, making it unaffected by the sequence order points point clouds or patches. Specifically: First, SLOA adopts the K-Norm mechanism for local feature propagation. This process only relies on the spatial relative distance between the central point and its neighboring points, rather than the se- quence order of neighboring points. Neighboring points with the same distance receive completely symmetric feature updates, eliminating order dependence at the source. Second, it aggregates neigh- borhood features through the K-Pool (Max-Pooling) operation. As typical cormmutationnnvariaan operation, Max-Poolin Pooolionss 's output result is only related to the maximum value of neighborhood fea- tures and has no correlation with the input order of features, further ensuring the stability of local aggregation results. Finally, in the offset attention calculation stage, the Query (Q), Key (K), and Value (V) matrices are all generated based on features fused with Spiking Position Encoding. Their dot-product attention calculation depends on the 3D spatial position similarity of points, and not the permutation order of Tokens in the sequence. At the same time, the offset between attention features and input features is calculated via element-wise subtraction, focusing only feature differences rather than order relationships. These three layers of symmetric designs collectively ensure that even if the global Spiking Mamba Block (SMB) path relies on Token sequences, the local geometric fea- tures extracted by SL.OA still maintain strict permutation invariance, fully adapting to the unordered characteristics of point clouds.
C THE RATIONALITY OF SPIKE MAMBA
We explain the rationality of Spike Mamba from two aspects: the parallel implementation of the IF neuron reset mechanism, and the time-step alignment scheme for Mamba.
C.I PARALLEL IMPLEMENTATION OF IF NEURON RESET MECHANISM
To address the compatibility issue between the IF neuron reset mechanism and parallel comput- ing in Mamba training, this study leverages the tensor-level batch processing capability of the Spikinglelly framework, converting the reset operation into a conditional element-wist assign- ment logic executable in parallel on GPUs. Specifically, for the neuron membrane potential matrix V BxxxxXX (where B denotes batch size, T denotes timestep, N denotes the number of point clouds, and C denotes feature dimension) at each timestep, the spike trigger matrix S +(( Vv)) is first calculated using a step function (where is the current membrane potential, and Vv, 1.0 is the optimal threshold, as shown in the ablation experiment in Section 4.2). Subsequently, parallel reset is performed based on S: V. u4 (1- St)) Vreset St (with Vreset 0). This process
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is implemented using the forch.where' function in PyTorch, which enables batch scheduling of re- set operations for all neurons using the SIMD (Single Instruction, Multiple Data) architecture of GPUs, avoiding efficiency losses caused by serial loops. In the Spiking Mamba Block (SMB) of 3DSMT, this reset process is executed synchronously with the Conv feature encoding and bidi- rectional SSM (L+SSM/C-SSM) state update processes of Mamba. That is, the spike generation and reset operations are embedded into the computation chain of Z1 = (Z(Z) (Equation 12) and Zs SN (Lin(Zi)) (Equation 13), with no additional time overhead, ensuring overall training efficiency.
C.2 ALIGNMENT SCHEME BETWEEN IF NEURON TIMESTEP AND MAMBA DISCRETE TIMESTEP
The timestep = 3 of the IF neuron (used for spatio-temporal feature accumulation) and the "se- quence dimension" of Mamba (corresponding to the token sequence of point cloud patches) belong to different feature processing levels. We achieve their alignment through feature dimension map- ping in the Spiking Patch Embedding (SPE) module. The specific process is as follows: First, for each local patch 2 E R (where k is the number of points in the KNN neighborhood) of the input point cloud, is duplicated into an input F. E RRT'xxx with = 3 timesteps. The membrane poten- tial matrix U E TR. X containing spatio-temporal information is obtained through computation using the MLP and SN layers of SPE. Subsequently, the MaxPool operation (Equation 2) is applied to aggregate the features of the T timesteps, resulting in global patch feature G E g* This op- eration preserves key geometric information across different timesteps while compressing the time dimension. Finally, U and G' are concatenated and processed through MLP and SN layers to output U" E R (where L is the number of center points sampled by FPS). This output is stacked with the learnable [CLS] token and Spiking Position Encoding (Spos) to form sequence of (L+1) 1) C. which serves as the input to Mamba. Through this process, the T = 3 spatio-temporal features of the IF neuron are effectively converted into the token sequence dimension processable by Mamba. This not only retains the event-driven characteristics of spiking computation but also achieves full compatibility with the linear-come complexxy global modeling capability of Mamba, avoiding conflicts between the time dimension and the sequence dimension.
D DATASET
ModelNet40 Wu et al. (2015) is a widely used benchmark dataset in point cloud processing, specif- ically designed for 3D object classification tasks. It contains 12,311 manually synthesized CAD models from 40 common furniture and daily item categories (e.g., tables, chairs, airplanes, cars, etc.). These models are generated by uniformly sampling point cloud surfaces, with each point cloud typically containing 10,000 points. ModelNet40 serves as a standard testbed for evaluating the generalization ability of point cloud classification algorithms, thanks to its clear object structures, high-quality point clouds, and moderate scale.
ScanObjectNN Uy et al. 2019) aims bridge the gap between synthetic datasets and real-world applications by providing 3D point cloud data directly scanned from real scenes. includes ap- proximately 15,000 object instances across 15 categories, with three key variants offering different challenge levels: OBJ_BG (Object with Background) contains target object point clouds cropped from scenes while retaining original background points to force models learn recognition amid noise; OBJ.ONLY (Object Only) provides 'pure' object point clouds with manually removed back- grounds, focusing on geometric identification; PB T50 RS (Partially Bounded with Trash objects, 50% rotation, and Sensor noise) the most challenging variant occlusion, background noise, irrelevant trash objects, 50% random sample rotation, and simulated sensor noise, evaluating model robustness in complex real-world messy scenarios.
point clouds with severe
ShapeNetPart Yi et al. (2016) is a subset of the ShapeNerCore Chang et al. (2015) dataset, focusing on the task of 3D object part segmentation. It is based on the ShapeNetCore dataset, with part annotations made on its models. It covers 16 object categories and contains 16,881 3D models. In addition to geometric data, the dataset also includes detailed part annotation information in the json format. ShapeNetPart plays an important role in research on 3D object part segmentation algorithms, providing strong support for training neural networks for accurate part segmentation of objects.
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A3miSArmeni et al. (2016) is classic dataset for 3D indoor semantic segmentation, collected from three different buildings at Stanford University, covering 6 independent areas (c.g., offices, corridors, laboratories). It stores data in point cloud format, including 13 common indoor semantic categories such as floors, walls, ceilings, furniture, and electrical appliances, with high annotation accuracy and strong scene diversity. It mainly used evaluate the performance of 3D point cloud semantic segmentation algorithms and serves as benchmark in indoor 3D environment understanding.
Semantic KITTI Behley et (2019) is outdoor 3D semantic segmentation dataset extended from the KITTI autonomous driving dataset. Its data is collected by on-board LiDAR under various real road conditions like urban areas, rural areas, and highways. It includes continuous point cloud data of 22 sequences. defining 28 semantic categories covering core targets in autonomous driving scenarios such as vehicles, pedestrians, bicycles, roads, and buildings. Featuring strong scene dy- namics, large data scale, and temporal information, it is commonly used for training and evaluating algorithms like 3D semantic segmentation and object detection in autonomous driving.
E MORE EXPERIMENT RESULTS
This section will provide a detailed introduction to the specific experimental setup and some supple- mentary experimental results.
E.I EXPERIMENT CONFIGURATION,
Experiments are conducted on server equipped with an AMD EPYC 7571 2.1 GHz 32-core pro- cessor and an NVIDIA RTX 3090 GPU with 24GB video memory. Our implementation is based on PyTorch and SpikingJelly Fang et al. (203)..
Classification on ModelNet40. We use N 1024 points as input and apply scaling and translation for data augmentation. The model is trained using the AdamW optimizer with cosine learning rate decay, an initial learning rate of 0.0005, a weight decay of 0.05, a batch size of 24, and 300 training epochs.
Classification on ScanObjeetNN. We employ rotation as data augmentation with a point cloud size of N = 2048. During training, utilize the AdamW optimizer with cosine decay, setting the initial learning rate to 0.0005, weight decay to 0.05, batch size to 24, and train for 300 epochs.
Few-shot Classification on ModelNet40. We adopt the ModelNet40 dataset and conduct experi- ments under the n-way m-shot setting, where n denotes the number of randomly sampled classes and m represents the number of samples per class. The model is trained using only n mn samples. During testing, 20 novel samples per class are randomly selected as test data. We evaluate combi- nations of n E {5, 10] and m E {10, 20). reporting the mean accuracy and standard deviation over 10 independent runs. The AdamW W optimizer with cosine decay is used with an initial learning rate of 0.0005, weight decay of 0.05, batch size of 24, and 150 training epochs.
Part Segmentation on ShapeNetPart. We utilize input point clouds of N = 2048 points without normals and employ cross-entropy as the loss function. Training is performed using the AdamW optimizer with cosine decay, an initial learning rate of 0.0002, batch size of 16, and 300 epochs. For both the S3DISAAmeni et al. (2016) and Semantic KITTI [Belley et al. (2019) datasets, the training configurations were set with reference to PTv3 Wu et al. (2024b) to ensure consistent and comparable experimental results.
ModelNet40
SeanObjettNN(PB T50 RS)
Methods
OA (%)
mAce (%)
OA (%)
mAco (%)
91.8
94.1
89.3
85.6
90.4
86.3
Table 12: Performance comparisons of different data augmentation methods on both the Model- Net40 and the ScanOhje:NN(PB.T50..R datasets.
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Spiking Neurons
OA (%)
(%)
Metric
Train
Test
LIF
93.8
90.9
Runtime
150s
27s
IF
94.7
91.8
Memory
10.1G
4.6G
Table 13: Effect of different spiking neurons to the accuracy performance the ModelNet40 dataset.
Table 14: Analysis Efficiency on the ModelNet40 dataset.
Num
OA
mAce (9))
OA (9)
mAcc (%)
93.7
90.3
512
93.4
90.2
8
93.8
90.6
1024
94.7
91.8
12
94.7
91.8
2048
94.1
90.8
16
93.9
4096
93.8
90.6
Table 15: Effect of SHB Number on ModelNet40
Table 16: Effect of point size on ModelNet40
E.2 DATA AUGMENTATION,
We define two different data augmentation methods: Translation-Scaling (TS) and Rotation (R). We also conduct comparative experiments on different datasets, with the results shown in the ta- ble 12 On ModelNet40,, the 3DSMT model with 'S data augmentation achieves improved per- formance, while the data augmentation method performs better on the most challenging variant dataset
of ScanObjectNN.
E.3 EFFECT OF DIFFERENT SPIKING NEURONS.
The core of SNN is spiking neurons. The IF neuron only requires accumulation and threshold comparison, making it easy to implement on neuromorphic chips with extremely low energy con- sumption, especially suitable for resource-constrained edge devices. The leakage mechanism of LIF neurons requires additional circuits to simulate membrane potential decay, increasing hardware complexity and energy consumption. We conducted ablation study the performance impact of these two neurons on 3DSMT, and the results are shown in the Table I The neuron is more suitable for application in large-scale complex point cloud models.
E.4 ANALYSIS ON EFFICIENCY.
We evaluate the efficiency of the 3DSMT model based on two metrics: runtime duration and memory consumption. The experimental equipment and specific configurations are described in Section Experiment Configuration. The ablation experiments adopt setting of 3 Timestep and calculate the results for one epoch. As shown Table 14] 3DSMT can implemented on a single GPU with 24GB video memory, featuring relatively short training and testing times and low video memory usage, providing new solution for large-scale point cloud processing.
E.5 EFFECT SHB NUMBER.
3DSMT employs a multi-block stacked Spiking Hybrid Block (SHB) architecture. To determine the optimal number of stacked blocks, we tested configurations with 4, 8, 12, and 16 blocks. Table [5 shows that 12 stacked SHB blocks achieve the highest classification accuracy of 94.7%. We argue that increasing the number of stacked SHB blocks enhances the model's ability to capture complex relationships in point clouds and effectively utilizes the sparsity of spiking signals to improve ef- ficiency. However, exceeding 12 blocks introduces redundant calculations, leading to a decline in generalization ability.
E.6 EFFECT POINT CLOUD SIZE.
Mainstream point cloud models commonly 1,024 uniformly sampled points. To assess the im- pact of sampling scale, we conducted ablation studies, revealing that 1,024 points offer the best trade-off between accuracy and efficiency (see Table 16).
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ScanOhjectNN OBJ.ONLY
Dataset
OBJ_BG
PB.T50_RS
OA (%)
90.8
89.7
90.4
mAce (%)
88.3
87.4
88.0
Energy (mJ)
4.5
4.5
4.9
Table 17: Energy Consumption of the Three Variants of the ScanObjectNN Dataset.
E.7 ANALYZE ON ENERGY CONSUMPTION OF THE THREE VARIANT DATASETS OF SCANOBJECTNN.
We calculated the energy consumption of the three variant datasets of ScanObjectNN (OBJ_BG, OBJ_ONLY, (B))5)_RS), with classification performance (OA and mAcc) as auxiliary evaluation metrics. The results are shown in Table 17 The energy consumption values the three vari- ant datasets of ScanObjectNN (OBJ_BC BBGOBJ//JBBBB WOLLOONNLLSSSSS RS) are 4.5 mJ, 4.5 mJ, and 4.9 mJ respectively.
E.8 ANALYSIS ON SPIKING ARCHITECTURE.
To more comprehensively evaluate the energy efficiency of 3DSMT point cloud analysis, we kept all structures of 3DSMT unchanged and only made one modification: removing all Spiking Neuron (SN) layers and replacing them with ordinary neurons using ReLU activation (to simulate the continuous activation characteristic of traditional Artificial Neural Network (ANN)), denoted as "3DSMT (without spiking)". The experimental results are shown Table 18 the full-precision floating-point 3DSMT (without spiking) achieves slightly higher accuracy than 3DSMT. However, the accuracy loss introduced by the spiking mechanism is negligible, whereas the resulting reductions energy consumption and computational load offer substantial advantages.
basee
Architecture
OA (%)
mAcc (%)
Flops
Energy (m.J
3DSMT (without spiking)
94.9
92.0
7.9
36.3
3DSMT (with spiking)
94.7
91.8
1.3
4.3
Table 18: Effect of different Architecture to the accuracy performance on the ModelNet40 dataset.
F COMPARISON TO ANN AND SNN-BASED METHODS
To clearly demonstrate the excellent balance between performance and energy consumption of 3DSMT, we conducted a multi-dimennional method comparison (as shown in Figure 5): When compared with SNN-based methods, 3DSMT achieved Icading OA accuracy in multple point cloud classification tasks such as ModelNe::40(MN40), OBJ BG (BG), OBJ ONLY (ONLY), and PB_T50_RS (RS). Meanwhile, it met the requirements of low FLOPs and low energy consumption, breaking through the trade-off bottleneck between accuracy and efficiency of traditional SNN, When compared with ANN-based methods, 3DSMT not only surpassed most ANN methods in OA accu- racy but also achieved extremely low FLOPs and energy consumption loss. The proposed 3DSMT demonstrates the comprehensive advantage of "high accuracy low energy" in point cloud pro- cessing, providing new ideas for the design point cloud models that balance performance and deployment requirements.
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based OA MN40)
V.S. ANN bused methods
OA(on MN40)
FLOPs
OAfon BG)
FLOPP
OA BG)
OAfon ONLY)
OAton ONLY)
OA(on
OAton
SpisePsintNet (CCV13)
SPM (50CV225
TranfomeerNiP<117
PoinfTramba (ICLR24)
P2SReaLNer (AAAF24)
(ours)
PointNet PoneeNet-NNNiP (NIP:17)
30SMT (ours)
(A4A195)
PoineMambaNNPV299
Figure 5: Comparison of proposed method with SNN and ANN based methods in terms of energy consumption, FL.OPs, overall accuracy (OA) on ModelNet40 dataset (denoted as MN40) and three variants of ScanObjectNN dataset, including OBJ_B (denoted BG). OBJ.ONLY (denoted as ONLY) and PB_T50_RS (denoted as RS).
G ENERGY CALCULATION
artificial neural network (ANN), the number of floating-point operations (FLOPs) an impor- tant metric for evaluating model computational complexity. In contrast, spiking neural network (SNN) use synaptic operations (SOPs) as the fundamental measure of computation. SOPs refer to the synaptic transmission process that occurs when spikes are generated. Unlike the continuous tivation in ANN, SNN only trigger computations when spikes occur, significantly reducing energy consumption. The calculation formula for SOPs is follows:
SOPs' Jr x FLOPS(I)
where / denotes the index of the spiking layer in 3DSMT; f. refers spike firing rate; is the time step; FLOPs(I) represents the number of floating-point operations (specifically multiply-and- accumulate operations, MAC); SOPs represents the quantity spike-driven accumulation (AC) operations. 3DSMT is designed with biological plausibility in mind, and idcally, it can be directly deployed on neuromorphic hardware. The inference energy consumption of 3DSMT can ex- pressed as:
ESDSII EMAC x (FLOPs) Comy FLOPsHc) EAC X
Σ SOPS'
(5)
where FLOP represents the floating-point operations of the first floating-point convolutional layer in the Spiking Patch Embedding module, and FLOPSPC represents the floating-point operations of the fully connected (FC) layers in various downstream tasks. We assume that both MAC and AC operations are implemented on SpikingJelly Fang al. (2023). where EMAC 4.6 pJ and FAC = 0.9 pl (Kundu et al. 2021b: Hu et al. 202 Horowitz
2014 Kundu et al. 2021a).
H VISUALIZATION
To explore the feature representation capability of the 3DSMT model, we use t-SNE to map high- dimensional features into 2D space for visual analysis of feature clustering effects across different datasets. As shown in Figure 6 feature points of the ModelNet40 dataset exhibit diffused distri- bution with partial inter-class overlaps, though core feature clusters show obvious clustering trends. Feature points of the OBJ_BG dataset form distinct cluster separations with clear class boundaries, while those the OBJ ONLY dataset are highly aggregated with minimal inter-class overlap. For the PB T50 RS dataset, although feature distributions slightly diffuse due complex noise, major
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GT
ModelNet4D
OBJ_8G
Earphone
Bag
Cap
Mug
Qurs
OBJ_ONLY ONLY
vB_T50_RS
Table
Skateboard
Pistol
Lamp
Rocket
Figure 6: Visualizing Classification Feature
Figure Visualization of Part segmentation results ShapeNetPart. Top line is our pre- diction and bottom line ground truth.
Learning on Different Datasets.
class clusters maintain compact structures. These results verify the strong capability of 3DSMT in capturing features of unordered point clouds.
We also provide more part segmentation visualizations to quantitatively demonstrate the effective- ness and performance of 3DSMT, as shown Figure 7 The part segmentation results of 3DSMT almost identical to the ground truth across different categories. Slightly complex categories may exhibit minor discrepancies, but these do not significantly affect the overall performance.
I CLAIM OF LINEAR COMPLEXITY FOR 3DSMT
The core of 3DSMT's linear complexity comes from two key architectural designs. The Spiking Lo- cal Offset Attention (SLOA) module does not the global self-attention of standard Transformers, which has complexity of O(N²). Instead, it only focuses on fixed number of K K-nearest neighbors for each point (K=4 in experiments). fies to O(N) because K is constant. The Spiking Mamba Block (SMB) is based on the selective state space model (SSM) core, which inherently has a sequential processing complexity of O(N). Therefore, the overall computational complexity of 3DSMT is dominated by the sum of these two linear operations, which is O(N) + O(N) = O(N). This ensures the model's 's linear complexity with respect to the number of input points (N).
corresponding complexity is O(N x K2), which simpli-
NEUROMORPHIC HARDWARE RATIONALITY
We expect to directly deploy 3DSMT neuromorphic hardware. However, designing RC circuits for processing 3D point clouds on neuromorphic hardware requires additional engineering efforts. Point cloud data typically exists floating-pon format, so is natural choice adopt an Em- bedding method with "multipy--ccumuulte (MAC)" operations at the network's 's initial stage to gen- erate spike signals for each point. Additionally, to pursue higher classification accuracy, the final fully connected (FC) layer needs to perform some MAC operations, which are difficult to implement on neuromorphic hardware. Although there is currently no method combining laser scanning with an event-driven mechanism to generate 3D point cloud spikes from the source, theoretically, our Spiking Local Offset Attention, Spiking Mamba Block, and Spiking Position Encoding can all be implemented neuromorphic hardware. They can convert floating-poit -pint point clouds into spike form before performing convolution (Conv) operations and execute accumulation (AC) operations accumulate the weights of postsynaptic neurons.
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K LLM USAGE STATEMENT
In accordance with the policy requirements of the ICLR 2026 Conference regarding the use of Large Language Models (LLMs), the authors of this paper hereby declare that: All core academic content of this discarch-includiee and analysis of experimental data, derivation of results, and refinement of conclusions was inde- pendently completed by human authors in its entirety, with no involvement of any LLM tools.
the formulation of research ideas, design of methodologies, collection
During the paper writing process, the authors did use LLMs for content generation, code writing, literature analysis, or result verification. Grammar checking of text expression and format adjust- ment were all completed manually, without assistance from any LLM tools such as ChatGPT, GPT-4 or Claude.
The authors confirm that there is no undisclosed LLM usage in this research, and all content complies with the requirements for academic integrity specified in the ICLR Code of Ethics. The authors bear full responsibility for the authenticity, aceuracy, and originality of this paper, and have verified through manual checks that there are factual errors plagiarism risks.
L ETHICS STATEMENT
We confirm that all authors have read and comply with the ICLR Code of Ethics. Our work does not involve human subjects, sensitive data collection or release, potentially harmful methodologies applications, conflicts of interest, discrimination/ias/aaanne legal compliance challenges, or research integrity matters that require explicit addressing.
concerns, privacy/security issues,
M REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our results, we will make our implementation code available as supplementary material. The datasets used (e-g.. ModeINet40, ScanObjectNN, ShapeNetPart for point cloud tasks) are publicly accessible, with relevant details provided in the main text. Key experimental settings (including hyperparameters like SNN threshold and timestep optimized in analysis) and procedures are thoroughly documented in the manuscript. Any theoretical derivations supporting our work included in the appendix.
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Reviews:
[{"review_id": "7SPTBtkMK8", "reviewer": "Reviewer_vEiz", "summary": "This paper presents a hybrid Spiking Mamba-Transformer architecture named 3DSMT for point cloud analysis. It includes two main components: (1) Spiking Local Offset Attention (SLOA) used to capture local geometry features; (2) Spiking Mamba Block (SMB) is designed for global, linear-time modeling for unordered point clouds. Extensive experiments on multiple benchmarks, including ModelNet40, ScanObjectNN variants, and ShapeNetPart, demonstrated strong accuracy and energy efficiency compared to previous SNN and some ANN baselines.", "questions": "1. Could the authors specify the exact voting mechanism and whether all baselines were evaluated under the same protocol?  \n\n2. Can you report per ScanObjectNN variants' energy cost with and without voting? \n\n3. In the Table 7, why does “No Order” outperform Z-order or shuffle? How do you serialize the input point cloud to satisfy the requirements of Mamba?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well organized; the figures are readable and understandable. \n\n2. The experimentation is quite complete and adequate and sustains the author's claims. \n\n3. The proposed method looks logical and technically sound.", "weaknesses": "1. The related work analysis of ANN-based point cloud models is incomplete; very highly relevant and recent works were not discussed in this section, such as Point Transformer V3, the Hybrid Transformer-Mamba model (PoinTramba), and so on.  The author should consider expanding the ANN-based related work and give a critical comparison. \n\n2. Table 1 shows a huge gap between “w/o voting” and “w/ voting” on ScanObjectNN dataset, but the paper does not specify why the voting mechanism heavily influences the performance.  \n\n3. The single-Transformer comparison primarily relies on Point Transformer v2; the experimental section does not include more recent/effective Transformer variants (like Point Transformer V3) as baselines on the three main benchmarks. This may weaken its superiority compared to recent ANN models. \n\n4. It seems the authors manually wrote the citation, and the format fails to comply with ICLR’s citation guidance. Please review the guidance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5hNVlXeRxm", "reviewer": "Reviewer_vvZF", "summary": "In this paper, authors proposed 3DSMT, a method that combined 1) Spiking Neural Network, 2) Transformer, 3) Mamba for point cloud analysis. Results on point cloud classification and part segmentation show the effectiveness of proposed method. In summary, this is a conventional work that combined several methods from different research topics without solid motivation.", "questions": "I would like to empahsize that combining multiple technologies from different domains and different research directions without solid motivation or strong empirical improvements cannot be considered as research.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "1. This work has a relatively good presentation.", "weaknesses": "1. Combining several methods from different topics and different domains without solid motivation is of no contribution. \n\nPlease consider the follow questions:\n\na. what is the motivation of  Spiking Neural Network for point cloud analysis without focusing on explainability, low-energy cost in the Experiment section?\n\nb .what is the motivation of Mamba block for point cloud analysis? Point cloud are naturally not causal ordered, and various solutions for model efficiency over Transformer, like linear attention, conv, MLP. Why consider Mamba block for point cloud?\n\n\n2. While authors emphasized efficient global context modeling, point cloud classification and part segmentation are not enough to demonstrate the efficiency. Authors shold conduct experiments on large-scale point cloud applications, like out-door LiDAR self driving scene. \n\n3. The baselines are work and strong baselines are ignored. For example, simple PointMLP and PointNeXt shown promising performance using only MLP, but these methods are ignored in Table 1.\n\nPointMLP: Ma, Xu, et al. \"Rethinking network design and local geometry in point cloud: A simple residual MLP framework.\" arXiv preprint arXiv:2202.07123 (2022).\n\nPointNeXt: Qian, Guocheng, et al. \"Pointnext: Revisiting pointnet++ with improved training and scaling strategies.\" Advances in neural information processing systems 35 (2022): 23192-23204.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5JPK9XfaYk", "reviewer": "Reviewer_7Z2Q", "summary": "In this paper, the authors propose a hybrid spiking machine learning framework called 3DSMT (3D Spiking Machine Learning Transformer) for event-driven 3D perception tasks. This method combines the energy efficiency of spiking neural networks (SNNs) with the global modeling capabilities of the Transformer, achieving efficient 3D representation learning of event streams through cross-modal fusion and spatiotemporal feature encoding. Experiments were conducted on multiple event datasets, including event camera 3D object recognition and dynamic scene understanding tasks, demonstrating a good balance between performance and energy consumption.", "questions": "See the weakness.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "This method combines the energy efficiency of spiking neural networks (SNNs) with the global modeling capabilities of the Transformer, achieving efficient 3D representation learning of event streams through cross-modal fusion and spatiotemporal feature encoding. Experiments were conducted on multiple event datasets, including event camera 3D object recognition and dynamic scene understanding tasks, demonstrating a good balance between performance and energy consumption.", "weaknesses": "1.\tThe structure diagram in Figure 1 is quite complex, and the input-output relationship logic of some modules is not intuitive enough.\n2.\tThe lack of independent ablation analysis for key modules makes it difficult to assess the contribution of each component. It is recommended to supplement this with a clear ablation comparison table (Baseline / +SNN / +Hybrid / +Full Model).\n3.\tFigure 4 shows a lack of clear contrast, requiring layout adjustments to enhance readability.\n4.\tLack of performance on large-scale point clouds (such as SemanticKITTI, Nuscenes)\n5.\tFigure 1 and Figure 5 are duplicates; it is recommended to modify either one of them.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KmtfmnXvbC", "reviewer": "Reviewer_hWC7", "summary": "This paper proposes a hybrid architecture 3DSMT (Hybrid Spiking Mamba-Transformer), designed to achieve efficient and accurate point cloud analysis. The model combines spiking neural dynamics, state-space modeling (Mamba), and the Transformer attention mechanism, achieving a strong balance between performance and energy efficiency.\n\n● 3DSMT is composed of three core modules:Spiking Local Offset Attention (SLOA): captures local geometric features;\n\n●Spiking Mamba Block (SMB): performs global feature integration with linear complexity;\n\n●Spiking Position Encoding (SPE): encodes the spatial and temporal structure of unordered point clouds.\n\n Experiments on ModelNet40, ScanObjectNN, and ShapeNetPart demonstrate that 3DSMT achieves state-of-the-art (SOTA) performance among Spiking Neural Network (SNN) models.", "questions": "1.What is the processing order in the Spiking Mamba Block? Is it executed sequentially like traditional SSMs, or in parallel within each timestep? Given that Mamba inherently depends on token order, how does the model maintain global permutation invariance?\n\n2.The paper claims that 3DSMT achieves linear complexity, yet the architecture still includes Transformer modules, whose attention mechanism typically exhibits quadratic complexity with respect to the number of tokens. Why, then, is the complexity described as linear in the **abstract**? This point is particularly important and should be explicitly discussed, as it directly affects the validity of the complexity claim in both the abstract and the main text.\n\nAlthough there are some open issues and limitations, the paper demonstrates solid innovation, completeness, and clarity. I believe it represents a high-quality and meaningful contribution to the field.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1.The model elegantly integrates spiking neural networks, the Mamba dynamic mechanism, and the Transformer structure, effectively balancing accuracy and energy efficiency.\n\n2.The experimental validation is extensive and comprehensive: 3DSMT performs consistently well across both classification and segmentation tasks.\n\n3.The feature visualization is well presented and convincingly demonstrates the model’s representational capability.\n\n4.The ablation studies systematically evaluate the effects of timestep and data augmentation strategies, providing good empirical support.", "weaknesses": "1.The novelty is somewhat limited: compared with Spiking Point Mamba (SPM) or Spiking Transformer, 3DSMT appears more as an engineering-level integration rather than a fundamentally new learning paradigm.\n\n2.The paper lacks discussion on real-time performance: latency and inference speed are not analyzed, which are crucial for SNN and neuromorphic applications.\n\n3.Scalability to large-scale point clouds is not validated — experiments are limited to inputs of 1K–2K points, and it remains unclear how the method performs on significantly larger point sets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)