# Spiking PointNet: Spiking Neural Networks for Point Clouds

arXiv: https://arxiv.org/abs/2310.06232 · HTML: https://mlsanity.com/paper/2310.06232/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 57 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 30 of 33)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation](https://mlsanity.com/paper/2005.01807/) (2020)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration](https://mlsanity.com/paper/2106.06984/) (2021)
- [Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks](https://mlsanity.com/paper/2105.11654/) (2021)
- [DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks](https://mlsanity.com/paper/2008.03658/) (2020)
- [Online Training Through Time for Spiking Neural Networks](https://mlsanity.com/paper/2210.04195/) (2022)
- [Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks](https://mlsanity.com/paper/2007.01204/) (2020)
- [Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/2304.05627/) (2023)
- [Optimized Potential Initialization for Low-latency Spiking Neural Networks](https://mlsanity.com/paper/2202.01440/) (2022)
- [Reducing ANN-SNN Conversion Error through Residual Membrane Potential](https://mlsanity.com/paper/2302.02091/) (2023)
- [Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes](https://mlsanity.com/paper/2302.10685/) (2023)
- [A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule](https://mlsanity.com/paper/1812.06574/) (2018)
- [Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization](https://mlsanity.com/paper/2305.02099/) (2023)
- [Membrane Potential Batch Normalization for Spiking Neural Networks](https://mlsanity.com/paper/2308.08359/) (2023)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks](https://mlsanity.com/paper/2308.06787/) (2023)
- [Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning](https://mlsanity.com/paper/2109.01905/) (2021)
- [ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks](https://mlsanity.com/paper/2306.03693/) (2023)
- [Real Spike: Learning Real-valued Spikes for Spiking Neural Networks](https://mlsanity.com/paper/2210.06686/) (2022)
- [LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor](https://mlsanity.com/paper/2208.02253/) (2022)

## Built on by (most cited first, 30 of 34)

- [Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks](https://mlsanity.com/paper/2312.06372/) (2023)
- [EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2403.12574/) (2024)
- [Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks](https://mlsanity.com/paper/2401.04486/) (2024)
- [Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformer](https://mlsanity.com/paper/2503.00226/) (2025)
- [Rethinking Spiking Neural Networks from an Ensemble Learning Perspective](https://mlsanity.com/paper/2502.14218/) (2025)
- [Efficient 3D Recognition with Event-driven Spike Sparse Convolution](https://mlsanity.com/paper/2412.07360/) (2024)
- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Spiking Point Transformer for Point Cloud Classification](https://mlsanity.com/paper/2502.15811/) (2025)
- [Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation](https://mlsanity.com/paper/2311.09077/) (2023)
- [PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining](https://mlsanity.com/paper/2505.05307/) (2025)
- [Efficient Spiking Point Mamba for Point Cloud Analysis](https://mlsanity.com/paper/2504.14371/) (2025)
- [ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks](https://mlsanity.com/paper/2506.07720/) (2025)
- [Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models](https://mlsanity.com/paper/2607.29048/) (2026)
- [Temporal Reversal Regularization for Spiking Neural Networks: Hybrid Spatio-Temporal Invariance for Generalization](https://mlsanity.com/paper/2408.09108/) (2024)
- [Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting](https://mlsanity.com/paper/2410.07266/) (2024)
- [Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation](https://mlsanity.com/paper/2412.13610/) (2024)
- [SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection](https://mlsanity.com/paper/2502.00074/) (2025)
- [Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud Denoising](https://mlsanity.com/paper/2502.19660/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions](https://mlsanity.com/paper/2605.20680/) (2026)
- [SVL: Empowering Spiking Neural Networks for Efficient 3D Open-World Understanding](https://mlsanity.com/paper/2505.17674/) (2025)
- [Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models](https://mlsanity.com/paper/2604.25570/) (2026)
- [Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net](https://mlsanity.com/paper/2411.14585/) (2024)
- [Activation-wise Propagation: A One-Timestep Strategy for Spiking Neural Networks](https://mlsanity.com/paper/2502.12791/) (2025)
- [SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding](https://mlsanity.com/paper/2506.07737/) (2025)
- [Rethinking Convergence in Deep Learning: The Predictive-Corrective Paradigm for Anatomy-Informed Brain MRI Segmentation](https://mlsanity.com/paper/2510.15439/) (2025)
- [Taming the Light: Illumination-Invariant Semantic 3DGS-SLAM](https://mlsanity.com/paper/2511.22968/) (2025)
- [Robust 3DGS-based SLAM via Adaptive Kernel Smoothing](https://mlsanity.com/paper/2511.23221/) (2025)
- [Detecting AI-Generated Videos with Spiking Neural Networks](https://mlsanity.com/paper/2605.05895/) (2026)
- [MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement](https://mlsanity.com/paper/2605.16081/) (2026)

## Abstract (arXiv)

Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency, have drawn much research attention on 2D visual recognition and shown gradually increasing application potential. However, it still remains underexplored whether SNNs can be generalized to 3D recognition. To this end, we present Spiking PointNet in the paper, the first spiking neural model for efficient deep learning on point clouds. We discover that the two huge obstacles limiting the application of SNNs in point clouds are: the intrinsic optimization obstacle of SNNs that impedes the training of a big spiking model with large time steps, and the expensive memory and computation cost of PointNet that makes training a big spiking point model unrealistic. To solve the problems simultaneously, we present a trained-less but learning-more paradigm for Spiking PointNet with theoretical justifications and in-depth experimental analysis. In specific, our Spiking PointNet is trained with only a single time step but can obtain better performance with multiple time steps inference, compared to the one trained directly with multiple time steps. We conduct various experiments on ModelNet10, ModelNet40 to demonstrate the effectiveness of Spiking PointNet. Notably, our Spiking PointNet even can outperform its ANN counterpart, which is rare in the SNN field thus providing a potential research direction for the following work. Moreover, Spiking PointNet shows impressive speedup and storage saving in the training phase.