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REVIEW 4 major objections 4 minor 69 references

Efficient Spiking Point Mamba for Point Cloud Analysis

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Spiking Point Mamba claims to be the first Mamba-based spiking neural network for point clouds, improving on prior spiking models by 6.2–7.4 percentage points while using at least 3.5x less energy than its non-spiking counterpart.

desk verdict First Mamba-based SNN for point clouds with real components and plausible ablations; headline deltas over SPT need a baseline re-run and a table audit before they are treated as established. read the letter →

arxiv 2504.14371 v2 pith:ZEDS5JG7 submitted 2025-04-19 cs.CV

Peixi Wu , Bosong Chai , Menghua Zheng , Wei Li , Zhangchi Hu , Jie Chen , Zheyu Zhang , Hebei Li
show 1 more author
Xiaoyan Sun
This is my paper · ORCID
classification cs.CV
keywords SpikingneuralnetworksPointcloudanalysisMambaStatespacemodelsHierarchicaldynamicencodingBlockEnergy-efficientinferenceMaskedpre-training
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that a spiking neural network (SNN) can get the long-range sequence modeling of Mamba without giving up event-driven efficiency, and that this combination is the right way to push 3D point-cloud accuracy in the SNN world. It proposes Spiking Point Mamba (SPM), built from a Hierarchical Dynamic Encoding (HDE) that makes the input tokens change over time and a Spiking Mamba Block (SMB) that adapts Mamba's gating and state-space branches to binary spikes. On top of that it adds masked spike-based pre-training with a small ANN decoder. The reported results are large gains over the previous SNN state of the art—up to +7.4% overall accuracy on ScanObjectNN and +1.9% instance mIoU on ShapeNetPart—with at least 3.5x lower energy than the ANN counterpart. A sympathetic reader cares because if these numbers hold, energy-efficient spiking models become a realistic option for point-cloud tasks rather than a niche with a large accuracy penalty.

What carries the argument

The load-bearing mechanism is the Spiking Mamba Block (SMB): a Mamba block rewritten so that its state-space branch and gate branch operate on binary spikes from Leaky Integrate-and-Fire (LIF) neurons. A LIF neuron accumulates a leaky membrane potential, emits a spike when the potential crosses a threshold, then resets. In SMB, the SSM branch reverses the time dimension of the spike feature before passing it through the selective state-space model, so the model sees past and future time steps, while the gate branch uses element-wise average pooling over the token dimension before the Hadamard product, a change the paper claims prevents the information loss that raw spike-gating causes. HDE supplies the SMB with a time-varying input stream, and the asymmetric SNN-ANN encoder-decoder pre-training keeps the SMB encoder spike-driven during inference.

What would settle it

Re-run the previous state-of-the-art spiking point transformer with its original hyperparameters and data splits, and compare directly with SPM; if its accuracy reaches or exceeds the self-reproduced numbers SPM improves on (82.8, 83.4, and 78.0 on the three ScanObjectNN variants), the claim of +6.2 to +7.4 point gains collapses. Separately, measure SPM's energy on real neuromorphic hardware or with an independent MAC/AC counter; if the ratio to PointMamba falls below 3.5x at four time steps, the efficiency claim is not general.

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Extended reading notes

Core claim

The central claim is that the poor performance of previous 3D SNNs comes from three fixable problems: static input encoding, missing long-range dependencies, and information loss when spike tensors pass through gates and state-space updates. SPM fixes the first with HDE, which splits farthest-point sampling into early, middle, and late stages and slides or extends the selected points across time steps so each time step sees a slightly different, hierarchically meaningful subset. It fixes the second and third with SMB, in which the SSM branch scans spike features with time-dimension reversal (Time Flip) rather than token reversal, and the gate branch averages gating activations across tokens before the Hadamard product to avoid dropping sparse spike information. The paper reports that the full SPM reaches 90.2/89.5/84.2 overall accuracy on the three ScanObjectNN variants and 92.3% on ModelNet40, exceeding the self-reproduced Spiking Point Transformer by 7.4/6.1/6.2/0.9 points, and 84.8% instance mIoU on ShapeNetPart, with 12.6x and 3.5x energy reductions at one and four time steps versus PointMamba.

Load-bearing premise

The headline gains are measured against a Spiking Point Transformer baseline that the authors re-ran themselves; if that re-run was under-tuned relative to the original model, the reported +6.2, +6.1, and +7.4 point improvements could be inflated.

Editorial extensions

If this is right

  • If the reported accuracies hold, a 12-layer spiking Mamba backbone can match or approach ANN point-cloud classifiers on ModelNet40 (92.3% versus 92.4% for PointMamba) while running spike-driven, event-based computation.
  • The +2.3 and +0.8 point gains from masked spike pre-training on ScanObjectNN and ModelNet40 imply that SNN encoders can be improved by ANN decoders without sacrificing spike-driven inference.
  • The 3.5x (12.6x at one time step) energy advantage suggests that at equal accuracy, the spiking alternative is the lower-energy choice on neuromorphic or sparse-accumulation hardware.
  • Because Mamba's state-space scan is linear in sequence length, SPM inherits a path to longer point sequences and larger point clouds than quadratic-attention spiking transformers.
  • The time-flip rather than token-flip ablation indicates that the temporal dimension is the useful axis for bidirectionality in spike-based point-cloud Mamba, a design rule for future SNN sequence models.
  • The asymmetric SNN-ANN pre-training recipe—spiking encoder, lightweight ANN state-space decoder, Chamfer Distance reconstruction—provides a ready-made template for self-supervised learning in other 3D SNN backbones.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: HDE's stage-wise treatment of farthest-point sampling should transfer to other ordered point-cloud tokenizers, such as octree or grid orderings, so the dynamic-encoding benefit is likely not specific to FPS.
  • Editorial inference: the paper's energy math assumes a 4.6 pJ MAC versus 0.9 pJ AC at 32-bit precision; under lower-precision ANN MACs the 3.5x margin would shrink, so the efficiency claim is hardware-precision-dependent.
  • Editorial inference: the mask-ratio peak at 0.6 and the Time-Flip result suggest a simple recipe for future spiking sequence models: keep the temporal axis bidirectional, keep the token axis unidirectional, and pre-train with a moderate mask.
  • Editorial inference: the supplementary neuron ablation shows that swapping in ILIF raises SPM to 93.0% on ModelNet40 and 85.2% on PB-T50-RS, so adopting ILIF as the headline configuration would widen the reported margins over prior SNNs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes Spiking Point Mamba (SPM), a Mamba-based spiking neural network for 3D point cloud classification and segmentation. It introduces three components: Hierarchical Dynamic Encoding (HDE), which makes direct encoding time-dependent by varying the point sampling across time steps; a Spiking Mamba Block (SMB) that adapts the Mamba gate and SSM branches to spike-based computation with a time-flip bidirectional strategy; and an asymmetric SNN-ANN masked pre-training scheme with an ANN decoder. On ScanObjectNN, ModelNet40, and ShapeNetPart, the authors report SNN state-of-the-art results, with gains of +6.2%, +6.1%, and +7.4% OA over Spiking Point Transformer (SPT) on the three ScanObjectNN variants and +1.9% instance mIoU on ShapeNetPart. They also report a theoretical energy reduction of at least 3.5x relative to the ANN counterpart. Component-wise ablations in Tables 4-6 support the individual design choices, and the supplementary provides additional ablations on neurons and masking ratios.

Significance. If the empirical claims are confirmed, SPM would be a useful contribution: it is the first Mamba-based 3D SNN, it combines temporal dynamics with state-space modeling, and its ablations isolate the effect of each proposed component. The energy model in Eqs. (18)-(20) is transparent as a first-order analytical estimate. However, the practical significance depends on the exact deltas against SPT, and those deltas currently rest on self-reproduced numbers without code or seeds, with theoretical rather than measured energy figures, and with an internal inconsistency in the reported ANN baseline. The manuscript is within scope for a computer-vision venue, but the headline claims need to be substantiated more carefully.

major comments (4)
  1. [Tables 1-3, §5.2.1] The headline improvements of +6.2%, +6.1%, and +7.4% OA and +1.9% instance mIoU are deltas against Spiking Point Transformer (SPT), and all SPT entries are marked as self-reproduced by the authors while Ref. [52] is the authors' own prior work. No code, random seeds, or reproduction details are provided, so the reader cannot determine whether the margin reflects the proposed architecture or a favorable re-implementation. I request the original SPT numbers (or a public checkpoint), a detailed reproduction protocol, and results from multiple seeds with mean and variance for both SPT and SPM before the SOTA claim can be accepted.
  2. [Tables 1, 4, and 7] Tables 1, 4, and 7 report inconsistent numbers for the same ANN baseline: PointMamba is listed as 90.2/89.8/85.4/92.4 in Table 1, 'ANN Mamba' is listed as 90.2/89.5/84.2/92.3 in Table 4, and Table 7 uses 90.2/85.4/92.4 (only three columns). Because Table 4 is used to claim that SPM's SMB performs on par with Mamba while reducing energy, these discrepancies must be corrected and the exact configuration (including whether the stars indicate self-reproduction) clarified.
  3. [§4.3, Eqs. (18)-(20), Table 7] The 'at least 3.5x lower energy' claim is a theoretical estimate computed from FLOPs and fixed per-operation pJ constants, not a hardware measurement. The text does not say how the mJ values in Table 7 are obtained (per sample, including memory/data movement, with which firing-rate statistics), and the energy constants are attributed to Ref. [1], which is a neuron-model paper rather than the usual source for these values. I recommend relabeling the claim as an analytical estimate and, if the energy advantage is a central contribution, adding measurements on a neuromorphic simulator or GPU.
  4. [Table 2, §5.2.1] The pre-training comparison with SPT is uncontrolled. Table 2 lists SPT at 82.6/92.5, but the text does not state whether SPT underwent the same masked pre-training on ShapeNet with the same mask ratio, epochs, and fine-tuning schedule, or whether this is the from-scratch number. Without this control, the claims that spike-based pre-training adds +2.3/+0.8 points over SPM from scratch and beats SPT by +3.9/+1.4 points are not substantiated.
minor comments (4)
  1. [Algorithm 1] Algorithm 1 uses S both as the input size to FPS and as the sampled point set, and the quantities L, M, R, E, and F are not defined consistently (for example, M is reassigned as M+L). Please restate the algorithm with distinct symbols and explicit index bounds.
  2. [Eqs. (12)-(13)] In Eq. (12), the left-hand side assigns two tensors U'_n and U'_t from a single SSM call; the time-flip indexing and the resulting shapes need to be stated explicitly, and Eq. (13) should explain how SN(EAP(Z_n)) broadcasts after average pooling over the token dimension.
  3. [§4.3, Ref. [1]] The reference for the energy constants is incorrect: Ref. [1] (Brette and Gerstner) does not report 4.6 pJ/MAC and 0.9 pJ/AC. Please cite the original source (for example, Horowitz 2014) or remove the citation.
  4. [Throughout] There are several typographical errors and reference issues: 'obversed' in Sec. 4.1.1, 'Segmantation' in Fig. 2, 'classfication' in Table 8, and Refs. [53] and [54] appear to duplicate the same paper. A careful proofread is needed.

Circularity Check

1 steps flagged · score 4.0 of 10

ANN Mamba ablation row duplicates SPM's own accuracy, making the ANN/SNN parity claim tautological; the SPT baseline is self-reproduced but not a formal circularity.

  1. renaming known result [Table 4 (Ablation on SMB), Sec. 5.3.1]
    "ANN Mamba - - - - 90.2 89.5 84.2 92.3 ... With these branches, SMB performs similarly to Mamba while reducing energy consumption."

    The 'ANN Mamba' row in Table 4 is numerically identical to SPM's own row (90.2/89.5/84.2/92.3), while the paper's actual ANN baseline PointMamba is reported in Table 1 as 90.2/89.8/85.4/92.4. The claim 'SMB performs similarly to Mamba' is therefore either a comparison of SPM to itself (parity by construction) or a mislabeled baseline that contradicts the paper's own Table 1. In either reading, the ablation evidence for ANN-SNN parity reduces to a renaming of SPM's own numbers rather than an independent ANN comparison.

full rationale

SPM's central contribution is an empirical architecture comparison, not a derivation. The main headline deltas (+6.2/+6.1/+7.4 over SPT) are measured against a self-reproduced baseline from the authors' own prior paper ([52], starred in Tables 1-3). This is a legitimate baseline-reproducibility concern about the magnitude of the claimed improvement, but it is not a formal circularity: SPM also exceeds the other listed SNN baselines by large margins, and the comparison is externally checkable in principle. The energy model (Eqs. 18-20) uses measured firing rates and standard per-operation energies; the pre-training and HDE ablations are empirical or supported by the paper's own supplementary measurements. The one concrete circular/renaming step is in Table 4: the 'ANN Mamba' row equals SPM's own row (90.2/89.5/84.2/92.3), while the paper's actual ANN baseline PointMamba is 90.2/89.8/85.4/92.4 in Table 1. The claim 'SMB performs similarly to Mamba' is therefore either SPM compared to itself or a mislabeled baseline; either way, the ANN-parity evidence reduces to SPM's own numbers. This is partial circularity in a secondary claim, not in the core classification/segmentation results.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim is an empirical architecture improvement; it does not rest on a mathematical derivation. The listed hyperparameters are chosen via ablations and affect the reported accuracy and energy. The main external inputs are the LIF model, the Mamba SSM, and the per-operation energy constants.

free parameters (5)
  • Time step T = 4 (default); 1,2,3,6 in ablation
    Chosen via ablation (Table 7); affects accuracy and energy, central to the SNN design.
  • Mask ratio = 0.6
    Chosen via ablation (Table 11) for pre-training; balances reconstruction difficulty.
  • Firing threshold V_th = 0.5 for most neurons; 0.25 before SSM
    Hand-set; influences spike sparsity and information retention.
  • Number of encoded points per time step E = 256 (128 for ModelNet40)
    Hand-set; affects tokenization and sequence length.
  • Early/late stage size = 10% of E
    Hand-set; determines HDE dynamics.
assumptions (4)
  • domain assumption LIF neuron model with surrogate gradient training is an effective SNN design.
    Used throughout (Section 3.1); standard in SNN literature.
  • domain assumption The S6 selective state space model (Mamba) provides strong sequence modeling that can be adapted to spikes.
    Basis of SMB (Section 4.1.3); cited from Mamba [11].
  • domain assumption FPS behavior divides into early unstable, middle stable, late redundant stages.
    Motivates HDE (Section 4.1.1); supported by supplementary Sec. A but not proven.
  • domain assumption Energy per MAC (4.6 pJ) and per AC (0.9 pJ) from prior literature.
    Used in Section 4.3; the paper cites [1] (Brette & Gerstner 2005), which likely does not contain these values.

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Cite this review

Pith. "Pith review of Efficient Spiking Point Mamba for Point Cloud Analysis." pith.science (2026). https://pith.science/paper/ZEDS5JG7

@misc{pith2026250414371,
  author       = {Pith},
  title        = {Pith review of: Efficient Spiking Point Mamba for Point Cloud Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZEDS5JG7}},
  note         = {Machine review of arXiv:2504.14371}
}
read the original abstract

Bio-inspired Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing 3D SNNs have struggled with long-range dependencies until the recent emergence of Mamba, which offers superior computational efficiency and sequence modeling capability. In this work, we propose Spiking Point Mamba (SPM), the first Mamba-based SNN in the 3D domain. Due to the poor performance of simply transferring Mamba to 3D SNNs, SPM is designed to utilize both the sequence modeling capabilities of Mamba and the temporal feature extraction of SNNs. Specifically, we first introduce Hierarchical Dynamic Encoding (HDE), an improved direct encoding method that effectively introduces dynamic temporal mechanism, thereby facilitating temporal interactions. Then, we propose a Spiking Mamba Block (SMB), which builds upon Mamba while learning inter-time-step features and minimizing information loss caused by spikes. Finally, to further enhance model performance, we adopt an asymmetric SNN-ANN architecture for spike-based pre-training and finetune. Compared with the previous state-of-the-art SNN models, SPM improves OA by +6.2%, +6.1%, and +7.4% on three variants of ScanObjectNN, and boosts instance mIOU by +1.9% on ShapeNetPart. Meanwhile, its energy consumption is at least 3.5x lower than that of its ANN counterpart. The code will be made publicly available.

Figures

Figures reproduced from arXiv: 2504.14371 by the authors.

Figure 1
Figure 1. The SNN-adapted design of Spiking Point Mamba, com [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overview of Spiking Point Mamba (SPM), which consists of Hierarchical Dynamic Encoding (HDE), Spiking Embedding [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Ablation study of the design of SMB. Model I-III denote three variant models of SMB while Mamba denotes ANN counterpart. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Qualitative results of part segmentation of our SPM and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Qualitative results of reconstruction on ShapeNet. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Quantitative comparison of the sampling structures of [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Qualitative results of part segmentation of our SPM and [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Qualitative results of reconstruction on ShapeNet. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.