Spiking Discrepancy Transformer for Point Cloud Analysis
Authors: Yijie Lu
Zhiyi Pan
Renrui Zhang
Yanhao Jia
Ronggang Wang
Zhaokun Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Spiking Neural Networks
Point Cloud Processing
Efficient Computing
Brain-inspired Computing
Primary area: applications to neuroscience & cognitive science

Abstract: Spiking Transformer has sparked growing interest, with the Spiking Self-Attention merging spikes with self-attention to deliver both energy efficiency and competitive performance. However, existing work primarily focuses on 2D visual tasks, and in the domain of 3D point clouds, the disorder and complexity of spatial information, along with the scale of the point clouds, present significant challenges. For point clouds, we introduce spiking discrepancy, measuring differences in spike features to highlight key information, and then construct the Spiking Discrepancy Attention Mechanism (SDAM). SDAM contains two variants: the Spiking Element Discrepancy Attention captures local geometric correlations between central points and neighboring points, while the Spiking Intensity Discrepancy Attention characterizes structural patterns of point clouds based on macroscopic spike statistics. Moreover, we propose a Spatially-Aware Spiking Neuron. Based on these, we construct a hierarchical Spiking Discrepancy Transformer. Experimental results demonstrate that our method achieves state-of-the-art performance within the Spiking Neural Networks and exhibits impressive performance compared to Artificial Neural Networks along with a few parameters and significantly lower theoretical energy consumption.

Published as a conference paper at ICLR 2026
SPIKING DISCREPANCY TRANSFORMER FOR POINT CLOUD ANALYSIS
Yijie Lu
ZZhiyi Pan
Renrui Zhang
'Yanhao Jia
'Ronggang Wang
ZZhaokun Zhou
!eeking University
TTinjin University
The Chinese University of Hong Kong
Nanyang Technological University
Tencent
pyyjjie@stu pku. pku.edu. cn,
zhaokunzhou@tencent.
ABSTRACT
Spiking Transformer has sparked growing interest, with the Spiking Self-Attention merging spikes with self-attention to deliver both energy efficiency and competitive performance. However, existing work primarily focuses on 2D visual tasks, and in the domain of 3D point clouds, the disorder and complexity of spatial informa- tion, along with the scale of the point clouds, present significant challenges. For point clouds, we introduce spiking discrepancy, measuring differences in spike features to highlight key information, and then construct the Spiking Discrepancy Attention Mechanism (SDAM). SDAM contains two variants: the Spiking Element Discrepancy Attention captures local geometric correlations between central points and neighboring points, while the Spiking Intensity Discrepancy Attention char- acterizes structural patterns of point clouds based macroscopic spike statisties. Moreover, we propose Spatially-Aware Spiking Neuron. Based on these, we construct hierarchical Spiking Discrepancy Transformer. Experimental results demonstrate that our method achieves saae--of-the-at performance within the Spik- ing Neural Networks and exhibits impressive performance compared to Artificial Neural Networks along with few parameters and significantly lower theoretical energy consumption.
INTRODUCTION
Spiking Neural Networks (SNNs), regarded as the third generation Neural Networks Maass 1997)
characterized by their biological plausibility Roy al. (2019). spike-driven characteristics, and low power consumption. By emulating the dynamics of biological neurons, SNNs utilize asynchronous binary spikes for information transmission, with the membrane potential updated only upon the arrival of spikes. This unique feature allows SNNs to avoid unnecessary computations on zero values, making them promising candidates on neuromorphic hardware, such as TrueNorth Merolla al. (2014) and Loihi Davies et al. (2018).
Researchers are making extensive efforts to enhance the performance SNNs across various visual tasks, including image classification Fang al. 2021b a): Guo et al. (2023): Meng al. (2023); x Xu cet al. (2024); Shen et al. (2024b), object detection Su et al. (2023): Luo et al. 2024): Qu et al. (2025). and semantic segmentation Yao et al. (2024a). Recently, inspired by the impressive achievements of vision transformers Dosovitskiy et al. (2021); Liu et al. 2021) in Artificial Neural Networks (ANNs), attempts have been made incorporate transformer- baseed architectures into SNNs. Notably, Spikformer Zhou et al. (2023c) introduces Spiking Self-Attention (SSA) mechanism, while the Spike- Driven Transformer Yao et al. (2024b) employs Driven Self-Attention. Other studies have focused on structural enhancements Yao et al. (2021): 1: Zhou et al. (2023a b): Zhang et al. (002bb); Qiu et (2024b); Deng al. (2024); Qiu al. (2025), training methodologies Wang et (2023b), and applications across different tasks Bal & Sengupta 2024). However, these efforts are primarily confined to 2D visual domains. The exploration the Spiking Transformer in 3D point clouds remains limited.
"Corresponding author.

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Published as a conference paper at ICLR 2026
Local Stage x
Global Stage x
Spiking Linear
Spliing Linear
Spiktng MLP
SpilongMLa
550
Lixox
Spiking Discrepancy Transformer
Spiking Limoar
Spatially. A Spiking Neumn
Spiking Liner
AP
EP9 KNN
Spiling
SyNee Liwe
KT
Discrepenoy
Matri Multiply
NP
Elemmnt wine Muluply
Sprang MLP
SEDA
SIDA
Spiking Sibb-sapling (SSU)
Figure I: The overview of Spiking Diserepancy Transformer and illustration of key components.
3D point clouds analysis is critical in autonomous driving Chen et al. (2017); Kidono et al. (201T): Havarro-Serment et al. (2010), scene understanding Chen al. (2019), and robotics Correll et al. (2016). SNNs leverage sparse spike features to replace the dense features used in ANNs, resulting in significant improvements in energy efficiency. However, the 3D point clouds exploration of SNNs remains quite limited, with existing approaches Lan et al. (2023b); Ren et al. (2023); Wu et al. (2024b) demonstrating inadequate performance. Furthermore, the architecture has yet to be designed to effectively integrate the biological characteristics and the spatial information. It is intriguing to explore the application of Spiking Transformers in the processing of point clouds.
However, using villina SSA and Spikformer Zhou et al. (2023c) to process 3D features is not ideal. There are primarily three challenges. Firstly, salient points representations located at object edges or at boundaries between different categories are critical for accurate prediction. However, SSA tends focus on highly similar points, smoothing even neglecting salient features, consequently degrading performance. Secondly, unlike the smaller number of tokens in 2D visual tasks, point clouds usually consist of a large number of points, making the use of SSA model global dependence computationally prohibitive. Furthermore, considering the redundancy in point cloud data, SSA is incapable simultancously capturing both local and global features. Overall, 3D features call for a redesigned, efficient attention mechanism that highlights discriminative (edge) features and seamlessly scales from local to global point-cloud feature modeling.
To address the above challenges, we propose Spiking Discrepancy Attention Mechanism (SDAM), which uses the spiking differences between the Query and Key as the attention matrix to enhance the representation capability for complex spatial structures. It consists of Spiking Element Discrepancy Attention (SEDA) and Spiking Intensity Discrepancy Attention (SIDA), applied in the shallow and deep stages, respectively. The SEDA captures local feature relationships between central points and neighboring points through fine-grained element-wise spiking differences. In contrast, the SIDA models global dependencies using coarse-grained differences in spiking intensity among central points. Additionally, we design a Spatially-Awar Spiking Neuron that effectively encodes spatial information in the initial membrane potential, compensating for the loss of spatial information in spike representations. Ultimately, we integrate SDAM with Spatially-Aware Spiking Neuron into hierarchical multi-stan stage local-global architecture, termed as Spiking Discrepancy Transformer, which is shown in Figure [] The main contributions are as follows:
We propose a Spiking Discrepancy Attention Mechanism (SDAM) tailored to the characteristics of point clouds. The mechanism includes Spiking Element Diserepancy Attention (SEDA) and Spiking Intensity Discrepancy Attention (SIDA), which effectively represent complex local-global spatial information.
We design a Spatially-Aware Spiking Neuron that encodes spatial information in the initial mem- brane potential, thereby compensating for the loss of spatial information in spike representations. The Spiking Discrepancy Transformer achieves state-of-the tfetthe-rtt performance among ENN-based approaches. Besides, our method's theoretical energy consumption significantly lower compared to ANN-based approaches.
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2 RELATED WORK
Deep SNNs and Spiking Transformers. Numerous studies focus on learning methods and archi- tectures for deep SNNs. Spatio-temporal backpropagation (STBP) Wu et al. (2018) enables the direct training of SNNs via backpropagation across both spatial and temporal dimensions. Temporal backpropagation Kheradpisheh & Masquelier (2020) calculates the gradients of spike timings with respect to the membrane potential at the moment of spike generation. Threshold-dependee normalization (tdBN) Zheng et al. (202T) facilitates the training of deeper SNNs. Additionally, JEW-ResNet(Fang et al. (2021b) proposes spiking element-wise residual mechanism to enhance the performance of deep SNNs. Spikformer Zhhou et al. (202cc) the first to transform all components of the Vision Transformer into spike- spike-based formulation, thereby pioneering the integration of SNNs with Transformer. Spike-driven Transformer Yao et al. (2044)) introduces linear-complexity spike- driven self-attention for efficiency. The following works focus structural enhancements Zhou et al.) (2023a/b) b): Wang et al. 2023a); Zhou et al. (2024a b). training methodologies Wang et al. (2023b), and applications across different tasks Bal & Sengupta (2024). Research the application of Spiking Transformers across various fields is gradually unfolding.
batch
Point Cloud Analysis. Point-based methods are pioneering in point cloud processing tasks, with numerous studies exploring 3D spatial convolutions [i et al. (2018), Yan al. (2020), point cloud spatial encoding Mohammadi & Salarpour (0024). and network architecture design Ma et al. (2022). Recently, the application of Transformers the point cloud have demonstrated significant potential, with the Point Transformer Guo et al. (2021); Zhao et al.
(2021) being a notable milestone. To enhance computational efficiency, PatchFormer Zhang al. (2022) proposes Patch Attention, Point Transformer V2 Wu et al.) (2022) introduces Grouped Vector Attention, and Flatformer Liu et al. (2023) further proposes Flattened Window Attention. The Point Transformer V3 /3|Wu al. (2024c) achieves state-of-the-art performance across various tasks. In SNNs, Spiking PointNet Ren et al. (2023) incorporates spiking neurons into the PointNet framework, but its performance remains suboptimal. P2SResLNet Wu et al. (2024b) combines 3D kernel point convolutions with spiking neurons, and E-3DSNN Qiu et al. (2024a) introduces Spike Sparse Convolution to efficiently extract sparse 3D features. However, these methods diverge from the biological simplicity and dynamics inherent in traditional SNNs. The ANN-to-SNN conversion approach LLan Lan et al. (2023a) also yields subpar results. Spiking Point Transformer Ww et al. (2024a) relies on single local feature extraction and fails to capture the global characteristics, leading to performance degradation. Currently, there an urgent need for coherent design from spiking neurons to spiking attention enhance the capabilities of the 3D Spiking Transformer.
3 METHOD
3.1 PRELIMINARIES
Spiking Neuron the basic unit of SNNs. We select the typical hard-reset Seckky-Integrat-ee (LIF) neuron Wu et al. (2018) as example,
H[t (1 1 - + 1 X(t]. Vin), V[t] = [+] (((()) + S(t),
(1)
Sit| ((H
(2)
(3)
where is the membrane time constant, and X(4] is the input current received at the time step L. When the membrane potential H[t] surpasses the threshold Vı. the spiking neuron will trigger a spike S[t] to subsequent layers. The heaviside step function (vv) is defined as 1 for > 0 and for O. Vit represents the post-spike membrane potential, which is either H t) if no spike is generated or is reset to Vresei upon spike event. It is worth noting that V is typically considered to be zero, whereas different insights may apply in 3D tasks.
Spiking Self-Attention is different from vanilla self-attention Vaswani al. (2017). Given the spike-form input X, the Query Q Key K, and Value V are in spike form. Besides, it discards the softmax normalization for the attention map, which can be described by the following equation:
Q = SN (L- (X)) K = SW (L-BN<((X))) V = S.V (L-BNV(XX)))
(4)
SSA K, V) = SNKQ * s),
(5)
where SN denotes the spiking neuron layer and L-BN represents that the features pass through Linear and Batch Normalization sequentially. is the scaling factor.
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3.2 SPIKING DISCREPANCY ATTENTION MECHANISM
The spatial features of unordered, irregular point clouds fundamentally differ from the semantic features of ordered, structured 2D visual data. SSA guides feature aggregation through dot product similarity, allowing points with similar features to be assigned higher weights. However, in 3D scenes, discriminative edge regions often exhibit stark changes in local geometric features, resulting in lower feature similarity scores. The smooth aggregation of SSA can lead to a dilution of scores in these critical regions, subsequently causing edge blurring effects. While spiking-base modeling brings efficiency, it inevitably introduces information loss. The presence of channel-wise spiking dot product mismatches within SSA exacerbates this issue, further degrading its representation capability, Moreover, the dot product-based SSA does not inherently satisfy translation invariance, which is crucial for 3D tasks. In summary, 3D analysis requires an efficient attention mechanism that focuses on distinct features and easily adapts from local to global point-cloud modeling. We design a Spiking Discrepancy Attention Mechanism (SDAM) tailored for 3D analysis.
SDAM(QQ. K, V) = SD(Q, K) V,
where SD is defined as the spiking discrepancy, which is a spike-driven feature metric and be obtained through subtraction between spike sequences. matrix operator which varies depending on the scope of the attention modeling. SD simu- lates the cortical neurons' response mechanism to asynchronous multi-channel spike misalign- ment through spike discrepancy sensitivity, where lateral inhibition of neighboring neuron ac- tivity enhances edge contrast. also satisfies the translation invariance of spiking features, as shown in Table [ by its ability to robustly recognize point clouds after spatial transformations. Inspired by the multi-stage hierarchical ar- chitecture, extend SDAM into hi- crarchical combination of Spiking Ele- ment Discrepancy Attention (SEDA) and Spiking Intensity Discrepancy Attention (SIDA). Specifically, SEDA captures the fine-grained spiking variation trends be- tween the center point and its neighboring points within a local point cloud cluster, while SIDA depicts the gearse-rrained sig- nificant differences at a macro scale among point cloud. The details will be elaborated in the following subsections.
represents spike-driven
Table I: Comparison of model performance degradation after spatial transformations on the datasets. The types of spatial transformations include random rotation, translation, and scal- ing. PT refers Point Transformer. OA and OAr respectively represent the original accuracy and the accuracy on the trans- formed datasets.
Models
Modelnet40
SeanOhjectNN
OA(%)
(Ap(5)
OA(%)
(Ar(3)
PT Zizo at 1202 HANN)
90.73300
56.4
PI Wav
20240 (ANN)
94.3
92.3-1.2)
87.9
65.6-211
SSA af
SNNN
89.8
86.20-366
83.2
80.402.8)
SDAMISNNI
92.5
91.2(-1.3)
86.2
85.00-1.2)
3.3 SPIKING ELEMENT DISCREPANCY ATTENTION
As shown in Figure I the Spiking Element Discrepancy Attention (SEDA) explicitly addresses the limitation of geometric feature dilution in edge regions by leveraging channel-wise spiking difference sensitivity, a bio-inspired mechanism that mimics lateral inhibition in cortical neurons Mao & Massaquoi (0077).
Unlike conventional dot- dot-product similarity that prioritizes smooth feature aggregation, SEDA operates on fundamental hypothesis: local geometric discriminability arises from spiking misalignment between neighboring points. The explanation is provided in the Appendix [ Formally, given a central query point spiking feature q 8T'C its n neighboring key points spiking feature set k == {ky E @x j = 1,2, n and central value point spiking feature where T represents time steps and C' denotes the number of channels, SEDA computes pairwise multi-channel spiking discrepancy,
SD, = q kj.
SD k) = SN
SD j *
(8)
SEDA k, v) = SD(q. vv.
(9)
where SD is denoted as spiking difference and SN represents the spiking neuron. s is a synaptic scaling factor. is the element-wise masking. means element-wise multiplication.
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To intuitively demonstrate the spatial representation capability of SEDA. perform t-SNE on the feature matrix obtained after applying SEDA and SSA on the ShapeNetPart Yi et al. (0116)
As shown in Figure 2 SEDA demonstrates superior geometrie discriminability com- pared to SSA. Specifically, SEDA induces two critical clustering properties: 1) Intra- part feature compactness, where points be- longing to same object part (e-g., chair legs or surfaces) form tightly cohesive clus- ters, and 2) Inter-part margin amplification, exhibiting enlarged separation distances be- tween clusters corresponding to distinct geometric components. In contrast, SSA features exhibit diffused distributions with overlapping clusters across object parts, in- dicating geometric ambiguity. These re- sults indicate that the network incorporat- ing SEDA successfully learns detailed spa- tial position features.
Figure t-SNE visualization of SSA and SEDA. Points of different colors correspond to distinct components of the ob- ject. We compress the features produced by SSA and SEDA in the first stage of the network.
3.4 SPIKING INTENSITY DISCREPANCY ATTENTION
Building upon SEDA's local geometric discriminability. we propose Spiking Intensity Discrepancy Attention (SIDA) capture global structural patterns through spiking intensity divergence, as shown in Figure D Spiking intensity represents the cumulative value of spikes for the current position features in a purely additive form. Unlike SEDA that focuses on micro-scale spiking discrepancy between neighbors, SIDA operates emerges from population-level firing intensity contrast across different point cloud regions. The explanation is provided in the Appendix C Formally, given the N N-points global spiking feature Q,K, V E STXNXC, SIDA can be written as follows:
macro-scale spatial hypothesis: topological significance
C
SD K) SN(( S ΣKT) *))
(10)
SIDA K, V) K) V.
(11)
By capturing global spiking intensity differences, SIDA effectively identifies macro- sncrr-structural patterns in point clouds, particularly the overall geometric layout and key object components. Specifically, SIDA's sensitivity to intensity differences addresses two critical challenges spiking-based point cloud analysis:
1) The intrinsic information loss in spiking features may compromise accurate 3D perception, while spik- ing intensity offers statistically robust solution for modeling 3D salient disparity regions. 2) The in- herent translation invariance of intensity divergence aligns seamlessly with 3D geometric priors, enhane- ing the model's ability to understand spatial struc- tures. To demonstrate that SIDA captures global features more effectively than SSA, we visualized the spiking point features, as shown in the Figure 3 SIDA produces sparse spike activations at critical lo- cations the point cloud skeleton, such as the wings, tail, and nose of aircraft; the lampshade, lamp post, and lamp base of a lamp. In contrast, SSA focuses on less important regions repeatedly emphasizes a particular component, such as the fuselage of an aireraft or only the lampshade of lamp.
Figure Visualization of SSA and SIDA feature map. The green point indicates each spike, while blue denotes silence. The feature map is derived from the spike matrix obtained after applying SSA or SIDA in the final stage.
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The hierarchical synergy between SEDA and SIDA forms bio-plausible computational mechanism for point cloud analysis. SEDA enhances local geometric discriminability in early stages, while SIDA models global topological structures through spiking intensity contrast in deep stages. This dual-attention framework enables the network to capture both fine-grained local details and global geometric relationships, improving object structure understanding.
3.5 SPATIALLY- AWARE SPIKING NEURON
Spiking features suffer from information loss when modeling complex spatial positions, and this issue becomes more severe as the depth of the network increases. To this end, we propose a Spatially-Aware Spiking Neuron (SASN) based on membrane potential dynamics, inspired by the characteristics of neuronal membrane potential dynamics and the specific traits of 3D tasks. The Initial Membrane Potential (IMP) affects neuronal dynamics [Shen et al. (2024b). Therefore, embedding spatial information into the IMP can enhance apatio-temport perception.
Specifically, due the decoupling of spatial point sampling from the neural network, we can encode the selected point coordinates into the IMP of spiking neurons using trigonometric functions Zhang al. (202b) in a nnn-leaaeabble manner before the network processing stage. In the first time step, according to Eq. the membrane potential changes from [X] to:
H[ll - (10-
+
(12)
where P()) represents the value the position encoding corresponding to the specific spiking neuron. Details on IMP position encoding can be found in the Appendix A SASN leverages the injection of spatial information from the IMP to achieve the spatio-temports information interaction in SNN-based point cloud processing. SASN can directly replace conventional spiking neurons, and we also conduct an ablation study on the insertion position.
3.6 OVERALL ARCHITECTURE
Based on the SEDA, SIDA and SASN, we construct a hierarchical Spiking Discrepancy Transformer (SDT), which is shown in Figure SDT is encoder structure consisting of multiple stages that progressively downsample and model the point set. Each stage contains the Spiking Sub-sampling Unit (SSU) and SDT block. SSU performs point set sampling and embedding cansformation. Point set sampling involves downsampling and center-nearest point sampling, implemented using Farthest Point Sampling (FPS) and K-Nearest Neighbors (KNN), respectively. FPS and KNN are common sampling methods without training parameters. For stage I, the output of point set sampling includes the centrol point features X R T corresponding neighboring point features X R x Nxxc and the original coordinates P/ R.X3, The embedding transformation is formulated as:
X Linear Lineer(S.N (SN((CT( X())),
(13)
Y, MP(X;) + AP(X(),
(14)
where CAT represents aggregation. After aggregating, the spiking points are projected into the neuron membrane potential X². Finally, by using Max-Average Pooling (MP, AP), we aggregate the local features from the neighborhood onto Y,. Each SDT block contains SDAM and Multi-layer Perceptron (MLP). First half of the stages employ the SEDA to extract local point cloud details and the others utilize the SIDA to extract global point cloud information. For 3D classification, the number of stages set to SDT by default employs the membrane potential shortcut residual Hu et al. 2023). Additional architectural details are presented in Appendix
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Building upon prior research in SNNs for point cloud analysis, we initially evaluate the performance of the proposed SDT for 3D classification. This evaluation encompasses experiments on both the synthetic ModelNet40 Wu et al.] (2015) and the real-world ScanObjectNN Uy et al. (011)). Furthermore, we showcase the versatility of SDT by extending its application to 3D semantic segmentation and object part segmentation tasks. For semantic segmentation, Large-Scale 3D Indoor Spaces (S3DIS) Armeni et al. (2016) which present significant challenges. For object part segmentation, we utilize the ShapeNetPart Yi al. (2016). To comprehensively assess
employ the Stanford

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Published as a conference paper at ICLR 2026
the performance of SDT, compare it with both conventional ANN methods and cutting-edge SNN approaches, including direct training methodologies and ANN-to- -NN conversion strategies. Finally, we perform ablation study to systematically analyze the contributions of individual components within SDT, thereby validating the efficacy of our proposed framework.
Table 2: Performance and theoretical energy consumption on 3D classification. * means self implementation
Method
Type
Param|b)
ModeNNe440
ScanDhJechNN
0A//977
miAce(S)
(AAR)
(BAcc(()))
Energyimi)
Pr al. A0l77a
[CMPR17]
3.47
68.20
63.40
2,47
PounNatta
NNuu1PS121
1.74
77.50
75,40
18.71
KPC
2019
RCCVI9)
15.20
92.10
85.
83.60
94.
PointTnnsskenng
84.07
RoutMLL
[[2]
12.00
94.10
91.30
72.36
Point- Dint-GPTCC Ch
[NuutPP223]
ANN
19.45
91.03
20.48
86.90
85
20:47
PocnG1 Zhing 0i
40250
[TMM24]
ANN
92.00
90,00
$5.50
84,90
PuintNee- La
20%
[CCV233]
ANN
3,50
$8.17
$4.02
0.26
65.36
60.33
0.27
KPCom SNN Wi
[AAA244]
ANN SNN
15.20
70.50
67.60
43.50
34,70
Spiking Pointee
eon
3023
[NeurPS23]
SNN
3.50
89.61
$4.20
0.24"
65.40
61.30
0.28"
P25Real Ne Wirrzr
[AAA124]
SNN
15.20
90.40
50.20
81.30
79.40
B E-JDSNN Ci0rie Virrteei AMXI
[AAI225]
SNN
3.27
91.70
$8.80
1.76"
83.91"
$1.02"
2.64"
SPI
[AAI25
SNN
9.64
91.43
89.39
13.5
K2.23
80.12
18.0
(T=(
SNN
2.25
92.18
68.92
0.45
X5.25
X3.20
0.61
(IT((=))
SNN
2.25
92.46
89.48
1.35
N6.19
84.37
2.11
Implementation Details. We implement SDT using PyTorch Paszke et al. (2019) on four RTX 4090
GPUs. For the neuron models, utilize those provided by the spikingJelly library Fang et al. (2023)
We construct the code framework based Zhang al. (2023a). Additionally, we specify the hyper- parameters used in our experiments. as summarized in Table 3 They are based on the common practices in the ANNs with slight adjustments. The initial point number refers to the quan- tity of point clouds fed into the net- work during the training process. In addition, the theoretical energy consumption formulation is provided in the Appendix D
Table 3:
of SDT on various datasets.
Parameter
ModelNes40
ScanOhjectNN
S3DIS
ShupeNeiPart
Learning Rate Weight Decay
1.5e
2e
le -
Se
Le -
le
Batch Size
32
32
96
48
Training Eguchs
300
300
100
200
Optimizer
SGD
Adam
Adama
Adam
Initial Point Number
1024
1024
24000
2048
4.2 3D CLASSIFICATION
Data and Metric. The ModelNet40 Wu al. (015) dataset contains 12,311 CAD models with 40 object categories. They are split into 9,843 models for training and 2,468 for testing. We follow the data preparation procedure of Qi et al. Qi et al. (017b) and uniformly sample the points from each CAD model. While ModelNet40 is widely regarded as the standard benchmark for point cloud analysis, its synthetic nature and the rapid advancement in point cloud methods may limit its relevance for modern approaches. Therefore, we also evaluate method on the ScanObjectNN benchmark Uyy eet al. (2019). ScanObjectNN includes approximately 15,000 objects, categorized into 15 classes with 2,902 unique real-world object instances. This dataset presents significant challenges for point cloud analysis due to factors such as background interference, noise, and occlusions. In our experiments, we focus on the most challenging perturbed variant called PB_T50_RS For evaluation metrics, we use the mean Accuracy (mAcc) within each category and the Overall Accuracy (OA) over all classes. The training and inference speed are provided the Appendix
Comparison with SNNs. SDT significantly outperforms existing SNN approaches in Table For instance, the previous best model. E- NNSNNN Qiu al. (2024a), achieves 91.70% OA on Med4 with 3.27M parameters, and SPT Wu et al. (2024a) reaches 82.23% OA on ScanObjectNN. In contrast, with only 2.25M parameters, SDT surpasses these models by 0.76% on ModelNet40 and 3.96% on ScanObjectNN, achieving both enhanced computational efficiency and improved performance.
Comparison with ANNs. As shown in Table 2 when compared to ANN-based methods such as, PointMLP Ma et al. (2022), Point-GPT Chen et al. (2024). and PointGT Zhang et al. (2024c), which utilize floating-poit point representations to encode richer information, SDT delivers competitive performance with only slight decrease in accuracy. Furthermore, our focus is leveraging SDT to minimize energy consumption during 3D point cloud processing while maintaining competitive classification accuracy. SDT achieves overall accuracies of 92.46% and 86. 19% on two benchmark
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datasets, respectively, closely matching the performance of PointMLP (94.10%) and Point-GPT (86.00%) Remarkably, SDT consumes only 1.8% of the energy consumption of PointMLP (1.33 mJ VS. 72.38 mJ) on ModelNet40,, 10.3% of the Point-GPT (2.11 mJ VS. 20.47 mJ) on ScanOb- jectNN. SDT also outperforms ANN-hased models such as PointNet++ and KPConv, requiring fewer parameters and less energy, demonstrating potential of SNNs for efficient 3D point cloud processing.
4.3 SEMANTIC AND OBJECT PART SEGMENTATION
Data and Metric. For the semantic segmentation task, we conduct experiments on S3DIS Ar meni al. (2016). The S3DIS dataset, designed for semantic scene parsing, consists of 271 rooms spanning Six areas across three buildings. In accordance with prior works Tchapmi et al. (0177; Qi et al. (2017)); Zhao et al. (202T), area 5 is excluded from training and reserved for testing. Following the standard evaluation protocol Qi et al. (2017b), we employ mloU, mean class-wise accuracy (mAce), and overall point-wise accuracy (OA) as evaluation metrics. For the object part segmentation task, we use the ShapeNetPart Yi et al.] (2016). It consists of 16,880 models from 16 shape categories, with 14,006 3D samples for training and 2,874 for testing. The number of parts for each category is between 2 and 6, with 50 different parts in total. We use the sampled point sets produced by Qi al. Qi et al. (2017b) for fair compar- ison with prior work. For evaluation metrics, we report category mloU and instance mloU.
Table 4: Semantic segmentation results S3DIS, evaluated on Area
PRC
AWN
03.3
13.6
06.5
633
360
09
23.1
36.
31.3
77.5
08.4
313
31.5
-8.5
186
12.3
2014
07.0
00.3
00.3
853
35.
03
60
09.
03.5
06.8
08.6
433
689
46]
462
ANN
170
66.6
681
68.0
485
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194
46.7
135.
212
74.3
563
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718
716
Ph
92.4
983
558
65.7
75.1
13.8
633
76.
79.4
65.
61.7
6857
387
100
98.1
76.
696
99.8
965
06.8
00
02
919
667
716
913
161
341
063
389
107
Performance Comparison. The results are presented in Table I and T We
Table 5: Object Part Segmentation results on ShapeNetPart.
compare our work with the previous state-
Mathod
Tyys
car. aloL())
its. máaU
Parun(
Energrin))
AMN
8.3
of-the-art ANN domain. Since no SNN has yet reported results on the S3DIS and ShapeNetPart datasets, we test the perfor- mance of the SNN state-of-the-ard method E-3DSNN Qiu et al. (2024a). For effective- ness and fairness, we use the case where T=1. On the S3DIS, our model achieves mloU of 69.6%, while E-3DSNN arrives at only 67.4%. On the ShapeNetPart, our model achieves a 2.0% improvement in category mIoU and a 1.3% improvement in instance mloU compared to E-3DSNN, obtaining the SOTA in the SNN area. Furthermore, method has competi- tive performance, approaching the SOTA accuracy of 73.6% mloU on S3DIS and 86.6% instance mloU on ShapeNetPart achieved by ANN-based methods, while the energy consumption is only 1.06% (7.29mJ vs (87.68mJ) and 3.73% (4.74mJ vs 126.96mJ) of ANN-SOTAs. We provide the training curve on S3DIS in the Appendix
DOCNN
AMN
12.3
85.1
1.5
23,1
Spiroet
ANN
81.7
35.3
2.2
41.4
PoiraCuss 1e 1-
A30%
82.8
85.7
1.7
15.4
PounCNN
AN6%
46.4
AMN
78
127.0
PoUMMP Mf
AMN
54.6
86.1
522
54.2
2007
ANN
86.1
86.2
6.8
102.3
SDYT
SNN
85.1
E.3
4.4 ABLATION STUDY
Ablation on Spatially-Aware Spiking Neuron. As shown in Figure 4 we investigate the effective- ness of integrating SASN into different components (two types of attention (SEDA and SIDA) and the downsampling block SSU). The baseline architecture uses Spiking Self-Attention (SSA) and the common LIF neurons. The results demonstrate that SASN enhances the model's 's spatial perception capabilities, particularly proving more effective in refining point cloud representations through SSU, SEDA, and SIDA. Besides, we compare other neurons with SASN in the Appendix
E5.5
Ablation on Attentions and Hierarchical Framework. As shown in Table T rows 1-4 demonstrate that both SIDA and SEDA are more effective than vanilla SSA in modeling point clouds with nearly identical parameters. Rows further indicate that, compared to using SIDA or SEDA individually, multi-level architecture where SEDA is employed in the early stages refine local point cloud
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Table 6: Ablation study on attention variants in differ- stages. The numbers marked in green and orange represent the of SEDA and SIDA, respectively, the corresponding stage.
96
ACCLETT
denotes the framework where all stages employ the SSA, while "None" refers to remove attention mechanism.
Attention Type
Stage Index (1-4)
ModelNer40)
Scan SannOhjeeNNN
None SEDA
12.334
85,25%
78.04%
Figure 4: The ablation study for SASN. We observe that the incorporation SASN into various modules consistently results enhanced performance.
12,3,4
92.34%
85.45 %
SIDA
1,2334
92.13%
84.73%
representations and SIDA is utilized in the later stages extract global information-acchee achieves superior performance in point cloud processing. More results are provided in the Appendix E.2
Table Ablation study on network depth and width.
Table Time Step Ablation.
Time Step
ModelNet40
StanObjeeeNN
Depth
Width
Parato())
MtdeaNetii
Ineegy(ns))
SeantigeatNN
(nergy(ni)
OA(S)
mAce(())
OA(S)
mAAc(())
[S4, [8]
0.50
0.35
177.0 M4
0:46
[43, 26]
0.32
11
0.35
$3.00
0.75
92.34
89.31
81.82
1921
0.73
91.93
92.46
89.48
88.71
86.19
85.53
84.37
83,70
[No, 768] [24.
[48, F6, 192, 384)
2.25
3.07
92.09
92.46
4.56
1.33
96.10
80
2.83
7,85
Time Step. As depicted in Table increasing time steps T' within a certain range improves accuracy. We set the maximum to in our study, the results are peaking at T=4 with 92.46% on the ModelNet40 and 86.19% the ScanObjectNN. As discussed in Wu al. (2024b), the results attributed to the scarcity of temporal cues in 3D datasets. Thus, increasing might lead to redundant computations without enhancing informative representation, Small time steps may be suitable for practical applications.
be
Ablation Network Depth and Width. Table B compares the parameter count, performance, and energy consumption of the SDT under varying depths and widths. The results demonstrate that excessively increasing either width or depth in point cloud tasks does not yield significant performance gains but escalates both model size and computational cost. Balancing the accuracy and efficiency, we adapt configuration with 4-stage depth and [48, 96, 192, 384] for width.
Sparsity Analysis. As demonstrated in Table 9 we analyze the sparsity of each component in the SIDA and SEDA, and their sparsity further corroborates the low energy consumption of SDT.
Robustness Analysis. In the Appendix E.4 and E,6 we further analysis the robustness of SDT.
4.5 VISUALIZATION RESULTS
Figure 5 piprssents the Semantic Segmentation of SDT on the S3DIS dataset. The predictions exhibit high degree of similarity to the ground truth, highlighting the effectiveness of our architecture in segmentation tasks. More visualization results are shown in the Appendix F
INPUT
SDT
INPUT
SDT
Table 9: The Sparsity of each Attention Type. Q K, V means Query, Key, Value Matrices, A means Attention Map. O means the results of Sell-Attention.
Atteation Type
((%)
K(%)
V(%)
A(%)
O(%)
3.12
6.82
SEDA
1.06
1.24
1.15
3.82
4.69
SIDA
1.55
1.48
1.56
3.75
4.93
Figure S: Visualization of segmentation on S3DIS. GT
means the ground truth.

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Published as a conference paper at ICLR 2026
5 CONCLUSION
For 3D tasks, design Spiking Discrepancy Attention Mechanism (SDAM), which includes Spiking Element Discrepancy Attention and Spiking Intensity Discrepancy Attention to model local- global spatial features. A Spatially-Aware Spiking Neuron is designed to align with the SDAM. Based on these, we propose the hierarchical Spiking Discrepancy Transformer (SDT). SDT achieves SOTA performance within SNNs and exhibits theoretically lower energy consumption compared to ANNs. SDT can further solidify the foundation for exploring the application of SNNs in 3D tasks, and also promote the design of next-generation neuromorphic chips for point cloud processing. The limitations and future work are discussed in the Appendix G
6 ACKNOWLEDGMENTS AND DISCLOSURE OF FUNDING
We sincerely thank Dr. Wei Fang for his assistance with this work. This work is supported by grants from the National Natural Science Foundation of China (62236009, 62206141, 62027804, and 62425101), and the major key project of the Pengcheng Laboratory (PCL2021A13). Computing support was provided by Pengcheng Cloudbrain.
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Hanle Zheng. Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li. Going Deeper With Directly-Trained Larger Spiking Neural Networks. In AAAI, pp. 11062-11070, 2021.
Chenlin Zhou, Liutao Yu, Zhaokun Zhou, Han Zhang, Zhengyu Ma, Huihui Zhou, and Yonghong Tian. Spikingformer: Spike-driven residual learning for transformer- based spiking neural network. arXiv preprint Xix:v:2304.111 11954, 2023a.
Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Zhengyu Ma, Huihui Zhou, Xiaopeng Fan, and Yonghong Tian. Enhancing the performance of transformer- spiking neural networks by improved downsampling with precise gradient backpropagation. arXiv preprint 4rXiv:2305.05954, 2023b.
Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Liwei Huang, Xiaopeng Fan, Li Yuan, Zhengyu Ma, Huihui Zhou, and Yonghong Tian. Qkformer: Hierarchical spiking transformer using qk attention. In NeurlPS, 2024a.
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net URL https::://pppeeeeeeee htt.ap/eeeeeeee.ett..oooomiiiiie /tt.:/orrmmiiiieeeeettttaaa
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A DETAILS OF INITIAL MEMBRANE POTENTIAL
We choose the x-coordinate 2, of the j-th point P. = (2), (2 2/) E R [1 in the N points as example.
UT (2)( 14 R
(15)
sin (z) a/(t B 3m
)), -===0 2n
[ cos (xy a/(l 8 )), 2n +1
(16)
UD] = Concat(U" UI, U ()), Ubi E n..
(17)
where means the channels, m E [0, (0. a and control the amplitude and wavelength of the trigonometric functions, respectively, and are set 1000 and 100 in experiments. t is the current time step, used to distinguish between different time points in the temporal dimension. Integrating the three coordinates, Pj is encoded into Ulj). The dimensional expanses from 3 to C. This parameter-free positional encoding maps the point cloud locations into high-frequency feature information, serving as the Initial Membrane Potential (IMP) for Spiking Neuron, which mitigates the SNN's inherent information loss.
B ARCHITECTURE DETAILS
B.1 POINT CLOUD CLASSIFICATION
Our Hierarchical Spiking Discrepancy Transformer (SDT) framework for point cloud classification shown in Fig. 1 in the main text. Herein, we provide additional details regarding the model. We use four stages. Each SSU module reduces the cardinality of the point set to in each stage. The K-Nearset Neighbor is set to 40. The initial encoding channels are set to 48, and the expansion in each stage is [2. 2. 2. 1]. For classification, we also perform global MAP (Max-Average Pooling) over the pointwise features to get global feature for the whole point set. This global feature is passed through a Spiking MLP with linear layer to get the global classification logits. We apply SEDA in the first half stages and SIDA in the last half stages.
B.2 POINT CLOUD SEGMENTATION
For dense prediction tasks such semantic segmentation, we adopt U-Net in which the encoder described in the main text is coupled with Spiking Feature Propagation decoder Qian et al. (2022). Consecutive stages in the decoder are connected by Spiking Points Propagation Unit. Their primary function is to map features from the downsampled input point set P2 onto its superset Pi 5 P2. To this end, each input point feature is processed by a Spiking Linear layer, and then the features are mapped onto the nigher-resolution point set P1 via trilinear interpolation. These interpolated features from the preceding decoder stage are summarized with the features from the corresponding encoder stage, provided via membrane shortcut skip connection.
For the segmentation head, the final decoder stage produces a feature vector for each point in the input point set. We also apply Spiking MLP and Linear layer map this feature the final logits. Besides, we use four stage for S3DIS Armeni et al. (2016) and ShapeNetpart Yi et al. (2016). In the S3DIS dataset, each SSU module reduces the cardinality of the point set to _ in each stage. The K-Nearset Neighbor is set to 32. The initial encoding channels are set to 48, and the expansion in each stage [2, 2, 1]. Each stage contains [4. 7, 4, 4] Transformer blocks, respectively. We apply SIDA in the final stage and SEDA in the other stages. In ShapeNetpart dataset, each SSU module reduces the cardinality of the point set 4 in the first stage while in the other stages. The K-Nearset Neighbor is set to 32. The initial encoding channels are set to 48, and the expansion in each stage is [2, 2, 2, 1]. Each stage contains only one Transformer block. We apply SEDA in the first half stages and SIDA in the last half stages.
C THEORETICAL ANALYSIS OF HYPOTHESES IN SEC 3.2
We provide a theoretical analysis of hypotheses in Sec 3.2. Prior ANN works have demonstrated that topological complexity of point clouds can be quantified by entropy Young & Wasserman (2001):
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Tang al. (2023) Jiang et al. (2021). Second, in the work Guo et al. (022a). it has been shown that the information in spiking features can likewise be measured by entropy. In ANNs, the variations in the coordinates of point clouds cause the extracted features within the point-cloud network to differ; similarly, in SNNs the spiking features exhibit the same situation. It shows that the geometric features related to spiking features. Thus, we further explain the hypotheses from the perspective of local and global entropy.
For SEDA, we propse hypothesis: "local geometric discriminability arises from spiking misalignment between neighboring points" at line 157. The validity of the SEDA hypothesis can be supported by analyzing the entropy and information content of local geometric features based on spiking misalign- ment within neighborhoods. Let E ST be the spiking feature of a query point, and {k, 1-1 be the spiking features of its neighboring points. We define the multi-channel spiking difference as SD, = q kj. and measure its magnitude dj SD). Normalizing over the neighborhood yields probability distribution:
dg Pj Σk= dx
(18)
The local geometric entropy is then defined as:
n Σ Pg poglogp
H Vocal
(19)
which quantifies the uncertainty of spiking misalignment distribution. A high entropy (nearly uniform P₂) indicates low geometric distinctiveness, while low entropy (dominated by few large d,) indicates salient local geometric features. Thus, the local geometric discriminability is inversely proportional Hoea and SEDA's purpose is highlight the local geometrie saliency.
For SIDA, we propse hypothesis: "topological significance emerges from population-level firing intensity contrast across different point cloud regions" line 187. We formalize macro-scale hypothesis by introducing Global Center Entropy which is computed across multiple cluster center points. In SIDA, the spike intensity of a given central point m is equivalent to the spike firing rate pm- We can normalize the firing rates of all points and subsequently compute the Global-Center-nnttee H Hglohal of point cloud M,
Pm
Qm
Σx Pk
(20)
M
H Hglobal
9m log 9m.
(21)
m=1
When the entropy H Hglobal is smaller, it indicates that the feature information content the point cloud is higher. This suggests that the network representation can capture the macro-level differences in the point cloud features. For SIDA's operation Jmn. SD (m, n) SN - -Pn) x max, q miny Ok
9m 4n.
Hoan X Emen SD(m, n). The total weight in SIDA is inversely proportional to Hgiohat. SIDA assigns higher weights to centers with "large firing rate differences leading to lower entropy," thereby directly capturing the macro-level topological salience of the point cloud.
D THEORETICAL ENERGY CONSUMPTION
According to the general convention of SNNs Panda et al. 2020)) Yao et al. (202)), we posit that the MAC and AC operations are executed on 45mm hardware Horowitz (2014), with energy consumption values of ECMAC = 4.6pJ and ECAC 0.9p.J per operation, respectively. The theoretical Energy Consumption (EC) of ANNs can be derived as follows:
ECANN 4.6pJ MACs.
(22)
In SNNs, the AC operations can be obtained by multiplying the MAC operations by the firing rate f of input spikes and the simulation time step T.
ACs MACS x f x T.
(23)
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In SpikingPoint, the operations of the first layer MACs map the floating-pon pratingg-oontt positions of the point cloud to spike features, while subsequent- operations are ACs for modeling sparse spiking-pore point features,
SP 4.6pJ x MACs' + 0.9pJ Σ ACS
(24)
where denotes the number of linear layers in the SDT. Note that we ignore the energy of BN, as it be incorporated into the linear layers during inference. Energy consumption for point cloud pre-processing is not accounted for, as does not involve SNNs computations.
E
MORE EXPERIMENTAL RESULTS
E.1
TRAINING AND INFERENCE SPEED
Table 10: Performance and Speed comparison the ScanOhjectNN dataset. OA denotes Overall Accuracy.
Model
Type
ScanObjectNN (OA(%))
Throughput (ins./sec.)
PointNet
ANN
68.20
4212
PointNet++
ANN
77.90
1872
KPConv
ANN
85.30
1281
PoinTTransformer
ANN
86.01
188
PointMLP
ANN
Point-GPT
ANN
86.90
134
PointGT
ANN
86.50
PointNext
ANN
87.70
2040
PointNet-SNN
ANN-to-SNN
66.56
4188
KPConv-SNN
ANN-to-SNN
43.90
1267
Spiking PointNet
SNN
1391
Net
SNN
81.20
E-3DSNN
SNN
83.91
245
SPT
SNN
82.23
168
SDT (ours)
SNN
86.19
279
As shown in Table 10 analyze the runtime performance of various models ScanObjectNN, along with their corresponding training and inference speeds. Our model, SDT, achieves state-of-the- art performance among SNNs while maintaining a competitive operational speed. When performing inference on GPUs. SNNs do not exhibit a clear throughput advantage over ANNs. However, SNNs are typically deployed on neuromorphic hardware, which significantly accelerates their execution and makes inference faster compared to ANNs.
Table 11:
More ablation study on various spiking attentions implemented on different stages. The numbers marked in green and orange represent the use of SEDA and SIDA, respectively, in the corresponding stage. SSA denotes the framework where all stages employ the SSA, while "None" refers to the framework where all stages consist solely of MLPs without any attention mechanism.
Attention Type
Stage Index (1-4)
ModeINet40
ScanObjeciNN
None
1,2,3,4
85.25%
78.04%
SSA
1,2,3,4
89.78%
83.20%
SEDA
1,2,3,4
92.34%
85.45 %
SIDA
1,2,3,4
92.13%
84.73%
SEDA+SIDA
1,2,3,4
92.46%
86.19% %
SIDA+SEDA
1,2,3,4
92.09%
85.05%
SEDA+SIDA
1,2,3,4
92.22%
85.34%
SEDA+SIDA
1,2,344
92.34%
85.22%
SIDA+SEDA
1,2,344
91.81%
84.94%
SIDA+SEDA
1,2,,4
91.41%
84.49%
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Epoch vs Validation mioU on S3DI5
Va Maldon nBU
Validation mtu
60
300
Epoch
Figure 6: Visualization of training the S3DIS dataset. GT means the ground truth. We choose the validation miou each epoch as y-axis.
E.2 ABLATION ON ATTENTIONS AND HIERARCHICAL FRAMEWORK
As shown in Table 11 rows 7,8 demonstrate that the SEDA and SIDA both play crucial roles. Excessive reliance on SEDA for extracting local information or on SIDA for extracting global information lead to performance degradation. Rows 6, 9-10 shows that SEDA and SIDA cannot be inverted; extracting global information first and then focusing on local details is counterintuitive, and experiments have also demonstrated that this approach leads significant performance degradation. This further validates the effectiveness of SDT.
E.3 EPOCH-ACCURACY CURVE
To further illustrate the performance of SDT, we present the validation mloU curve on the S3DIS dataset, demonstrating the variation with increasing epochs, as shown in Figure 6 Tab. in the main text reports the test mloU results.
E.4 CROSS-DATASE DATASET RESULTS
Table 12: Cross-dataset Results Point Cloud Classification
Train: ModelNet40 Test: ScanObjectNN
Train: ScanObjectNN Test: ModelNet40
Architecture
PointNet Qi ct al.
2017a
31.1
50.9
SpiderCNN Xu
2018
30.9
46.6
PointNet++ Q
20176
32.0
47.4
DGCNN Wang et
2019
36.8
54.7
PointCNN
2018
24.6
49.2
Simple Goyal
2021
40.5
57.9
SDT
51.3
66.2
According to the experiments in work Goyal et al. 2222T) we design cross-dataset evaluations based on our SDT. Our results surpass those of prior SNN-based point-cloud methods. This demonstrate that our SDT network exhibits strong cross-datase generalization.
E.5 ABLATION ON NEURON TYPES
As shown in Table 13
conduct experiments under the setting T=4. Our SASN outperforms the neurons you highlighted on both ModelNet40 and ScanObjectNN, indicating that the spatial information encoded in SASN is better suited to point-cloud tasks.
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Table 13: Ablation various Neuron
Neuron Type
ModelNet40 OA(%) (mm%)/m
ScanObjectNN
OA(%)/mAce($%
SASN
92.46/89,44
86.19/84.37
IMP Shen al. (2024a)
91.69/88.34
84.56/82.44
RealSpike Guo el al. (002b))
91.88/88.69
85.01/83.45
TernarySpike Guo (2024)
91.45/88.
84.91/83.22
MultiSpike
Q ct al. 2024a
91.96/89.01
85.34/83.81
E.6 ROBUSTNESS ABLATION STUDY
Table 14: Robustnss Performance Comparison of SDT, SEDA, SIDA, SSA and ANN-based models
Midels
Type
ACCase
ACCuse
Unifann
Guus.
Impulse
Upsamp.
Rg.
PointNe Oi al.
20176
ANN
90.7
67.3
$7.5
$5.6
70.9
96.0
6.4
PointNet=100
20176
ANN
93.0
78.5
79.6
83.6
61.99
52.8
$1.4
DGCNN
PaintM]
PCT 3-
2021
ANN
92.9
719
87.9
36.
60.9
82.6
42.1
Point Trmsuri wt
2021
ANN
93.7
78.0
89.9
88.2
69.9
74.3
67.7
PTV: al. Al2ee
ANN
94.5
360
91.3
90.0
77.4
86.8
84.4
SEDA
SIDA
SNN
92.1
87.8
90.4
90.0
89.3
81.2
85.3
SDT
SNN
92.5
90.4
91.2
91.5
91.8
88.2
89.2
Here, we analyze the noise robustness of the Spiking Discrepancy Attention Mechanism and its corresponding two attention variants. SEDA and SIDA. We compare them with SSA, well as with the ANN-based models, especially Point- Srint-Tranffommr and Trant-Transfommer v3, as shown in Table 14 Following the ModelNet40-C Gooal al. (2021) benchmark and previous work (Zhang et al. 2024a), our model is trained on the clean ModelNer40 dataset [Wu et al. (2015) and evaluated using its corrupted test sets. ModelNet40-C is constructed by applying various corruptions to the ModelNet40 test set, encompassing 15 distinct corruption types categorized into Density, Noise, and Transformation, each with five variations. Furthermore, each corruption type includes five severity levels. For our assessment, we focused on the noise category, specifically selecting Uniform, Gaussian, Impulse, Upsampling, and Background corruptions. We also use the overall accuracy for performance evaluation. The results demonstrate that SNNs combined with the Transformer architecture exhibit superior noise robustness. Within the category of SNNs, our designed SEDA and SIDA demonstrate enhanced noise robustness compared to the original SSA.
Table 15: Ablation studies and hyperparameter analysis on ModelNet40 and ScanObjectNN.
Analysis Type
Item
Value
ModelNet40
SeanOhjjectNN
0A(9)/mAco(S)
Ours (Default)
92.46/89.48
16.19/84.37
Spike Generation
Poisson Encoding
92.23/89.18
65.89/84.15
0.25
92.43/89.51
26.22/84421
Decay
1.0
92,75/89.77
85. 85.89/44441
0.5
92.56/89.91
K5.91/84.02
Neuron Hyperparameters
Threshold
1.5
92.26/89.11
86.01/841.7
Sigmoid SoftSign
92.13/88.
85.8 $5.85/83..0 669
Surrogate Function
91.99/88.41
85.56/83.99
Random Drop
91.87789.00
85 89/84.44
Spike Perturbations
Random Flip
91 31.55/88..34
85. 23.23/83.4 .45
Random Noise
91.72/88.52
85.71/83.75
Besides, we conduct more comprehensive robustness evaluations under various scenarios in Table 15
altering the spike encoding i..., spike generation settings (Poisson Encoding). the spiking- neuron hy- perparameters (Decay Factor, Threshold and Surrogate Function), and noise scenarios (perturbations and transformations in the spike feature). The "Spike Perturbations" refer to perturbations applied to the spiking features within the attention mechanism. "Random Drop" denotes the random dropping
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of spike features during inference. "Random Flip" denotes the random flipping of spike features during inference. "Random Noise" denotes the addition random noise to the spike features during inference. For hyperparameters, SDT adopts rate coding for spike generation, neuron threshold of 1.0, decay factor of 0.5, and uses Atan as the surrogate function. The results demonstrate that the SDT design is relatively robust various hyperparameters and noisy scenarios, exhibiting strong generalization.
F MORE VISUALIZATION RESULTS
F.1 VISUALIZATION OF SEDA
In Figure 7 we show more t-SNE visualization of spiking features, further proving that SEDA demonstrates superior geometric discriminability compared to the SSA.
F.2 VISUALIZATION OF SIDA
In Figure we show more spiking point features of SIDA and SSA to show that SIDA captures global features more effectively than SSA.
F.3
VISUALIZATION S3DIS RESULTS
In Figure 9
we show more Semantic Segmentation results on the S3DIS dataset.
F.4 VISUALIZATION OF SDAM AND SSA ATTENTION MAPS
In Figure 10 we compare the attention map distributions of SSA versus SDAM (SEDA and SIDA). The visualization results demonstrate that SDAM is capable of better capturing global point cloud information, particularly regarding the geometric distinguishability of edge features. This further validates the effectiveness of SDAM compared to SSA in point cloud tasks.
G LIMITATIONS AND FUTURE WORKS
As mentioned in Section 3.2 SDAM and SDT specifically designed for point cloud analysis. In the domains of images and text, these mechanisms may require further refinement to enhance their generalization capabilities. Future works will focus on designing SNNs for point cloud analysis in larger, open- open-world scenarios.
H USE OF LLMS
We declare that the LLMs are used solely to aid or polish the writing and are not involved in the development of the main methodology or comparative experiments.
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Object
SSA
SEDA
Object
SSA
SEDA
Figure 7: t-SNE visualization of SSA and SEDA. We utilize the feature maps obtained after the first stage of
SSA SEDA in the network and subsequently compress them into two-dimensional plane using L-SNE.
S
A
S
D
A
Figure 8: Visualization of SSA and SIDA feature map. The green point indicates each spike, while blue denotes silence. The feature map is derived from the spike matrix obtained after applying SSA or SIDA in the final stage.
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INPUT
SDT
GT
INPUT
SDT
GT
ociling
florr
wall
bram
onfurn
window
table
s/h
bookrse
Figure 9: Visualization of semantic segmentation results on the S3DIS dataset. GT means the ground truth. We selected six representative scenarios for validation.
High
Low
SSA
SIDA
SSA
SEDA
Figure 10: Visualization of SSA and SDAM Attention Map. High values indicate elevated attention levels, while low values suggest reduced focus. We observe that SSA tends to prioritize regions with high similarity, specifically the denser areas of the point cloud. However, these regions often fail to yicld effective features for classification. In contrast, SDAM (comprising SEDA and SIDA) gravitates towards regions with significant dis- siitty--pecifically areas exhibiting geometric discriminability. Features derived from these regions prove to be highly effective for identifying point cloud categories. This demonstrates that the difference-hassee measurement employed in SDAM is more effective for point cloud analysis than similarity- measurement used SSA.
23


Reviews:
[{"review_id": "pY193HubY7", "reviewer": "Reviewer_UkDr", "summary": "This paper proposes a hierarchical Spiking Discrepancy Transformer. Building upon spike feature discrepancies, authors construct a spiking discrepancy attention mechanism and design both spiking element discrepancy attention and spiking intensity discrepancy attention. Additionally, this work also introduce spatially-aware spiking neuron. Experimental results demonstrate the superior performance of the proposed model.", "questions": "Please refer to the Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written and provides a detailed presentations of the methodology and motivations.  \n2. The proposed model demonstrates superior performance on both point cloud classification and segmentation tasks, also significantly reduce parameter count and energy consumption.   \n3. The proposed spiking discrepancy attention is well-motivated with good theoretical foundations, and its effectiveness has been validated through comprehensive experiments.", "weaknesses": "1. A core contribution of this work lies in the design of spiking discrepancy attention. This fundamental innovation requires further validation across diverse Transformer architectures to demonstrate its generalizability.  \n\n2. The use of KNN and trigonometric functions in the spatially-aware spiking neuron raises concerns that the proposed model architecture may not be truly spike-driven[1].  \n\n3. The semantic segmentation experiments lack comparisons with SNN models.  \n\n4. Table 6 shows limited performance gains when comparing SEDA+SIDA against SEDA-only. Therefore, I am concerned that the computational burden introduced by SIDA may not justify such marginal performance improvements.  \n\n[1] Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips. ICLR24", "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": "A2SwCPrtLK", "reviewer": "Reviewer_krS2", "summary": "The paper introduces a Spiking Discrepancy Transformer (SDT) designed to enhance processing of 3D point clouds using spiking neural principles combined with self-attention. Traditional Spiking Transformers have mainly focused on 2D visual tasks, but 3D point clouds pose additional challenges due to their spatial disorder and scale. To address this, the authors propose a Spiking Discrepancy Attention Mechanism (SDAM), which measures differences in spike features to highlight key spatial information. Experiments show that the proposed method achieves state-of-the-art performance among SNNs, while significantly reducing energy consumption and parameter count.", "questions": "1. The model operates on static point clouds, and the benefits of temporal encoding remain marginal.\n2. More Results should be included in Figure 4 to prove the effectiveness of SASN.\n3. Provide a clearer explanation of why SNNs are particularly advantageous for point cloud tasks.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Method innovation: The paper presents a hierarchical Spiking Discrepancy Transformer (SDT) that combines bio-inspired spiking dynamics with multi-scale attention to capture both local and global 3D features. Its spiking discrepancy and spatially-aware design enhance feature discriminability, efficiency, and spatial robustness for accurate 3D perception.\n2. Paragraph Clarity: The paper demonstrates excellent narrative coherence and logical flow, presenting its ideas in a clear, progressive manner. The transitions between concepts are smooth, and the reasoning from problem to solution is both rigorous and intuitively convincing, enhancing the overall readability and scientific impact.\n3. Experiment Solidity: The researchers conducted classification and segmentation tasks, enabling the model capture 3D feature robust and accurate.", "weaknesses": "1. Although multiple timesteps are assessed, the model still handles static point clouds, leaving the added value of temporal encoding somewhat uncertain.\n2. The ablation experiment in Figure 4 does not explicitly indicate the baseline model and makes it difficult to validate the effectiveness of using SASN alone.\n3. It would strengthen the paper to provide a clearer explanation of why SNNs are particularly advantageous for point cloud tasks, beyond potential energy efficiency, and to demonstrate this advantage empirically.", "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": "rQstyx1Th1", "reviewer": "Reviewer_Aiaz", "summary": "This paper proposes the Spiking Discrepancy Transformer (SDT) for 3D point cloud analysis, with key contributions including the Spiking Discrepancy Attention Mechanism (SDAM), which consists of local SEDA and global SIDA variants, the Spatially-Aware Spiking Neuron (SASN) that encodes spatial information through the initial membrane potential, and achieving strong performance in the SNN domain across multiple datasets while claiming significant energy reduction.", "questions": "1.A more rigorous theoretical analysis or appropriate references must be provided to substantiate the challenges associated with applying SSA to 3D point cloud tasks.\n\n2.It is essential to validate the sensitivity of spiking discrepancy to edge features using synthetic data, or alternatively, to analyze and compare the attention map distributions of SSA versus SDAM in order to substantiate the fundamental claims made in the paper.\n\n3.In Eq. 8, why are the neighbors not weighted? The simple summation approach may allow distant neighbors to exert an undue influence, which requires further justification.\n\n4.The selection of the scaling factor s is not discussed, and the lack of ablation studies to support its choice undermines the robustness of the methodology.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "To address the challenges of existing SNNs in 3D point cloud tasks, a complete system is proposed, ranging from attention mechanisms to neuron design and overall architecture. Additionally, the experiments are comprehensive, covering multiple tasks including classification, semantic segmentation, and object part segmentation, with thorough ablation and visualization analysis.", "weaknesses": "1. The paper claims SSA applied to 3D point cloud tasks overlooks significant features and fails to capture local and global information, but lacks clear theoretical or experimental validation to support these claims.\n\n2. The paper asserts that \"spiking discrepancy\" better captures geometric discriminability but does not justify why disparity is more suitable for point clouds than similarity, relying solely on biological plausibility without rigorous proof.\n\n3. The presentations of SEDA and SIDA are not sufficiently clear: symbols are inconsistent, key variables and steps are ambiguously defined.", "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)