mlsanityData to 14 Oct 2026

Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation

arXiv 2311.09077 ↗First posted 15 Nov 2023cs.CVcited by 6

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Abstract (arXiv)

A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are continuous functions, however, real geometry or density field is frequently discontinuous at the interface between the air and the surface. Such a contrary brings the problem of unfaithful geometry representation. To this end, this paper proposes spiking NeRF, which leverages spiking neurons and a hybrid Artificial Neural Network (ANN)-Spiking Neural Network (SNN) framework to build a discontinuous density field for faithful geometry representation. Specifically, we first demonstrate the reason why continuous density fields will bring inaccuracy. Then, we propose to use the spiking neurons to build a discontinuous density field. We conduct a comprehensive analysis for the problem of existing spiking neuron models and then provide the numerical relationship between the parameter of the spiking neuron and the theoretical accuracy of geometry. Based on this, we propose a bounded spiking neuron to build the discontinuous density field. Our method achieves SOTA performance. The source code and the supplementary material are available at https://github.com/liaozhanfeng/Spiking-NeRF.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 48 of its 76 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

48 of 76 references.

References · 48

  1. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  3. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  4. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
  5. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
  6. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  7. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
  8. 2021Volume Rendering of Neural Implicit Surfacescited by 996
  9. 2020Neural Sparse Voxel Fieldscited by 959
  10. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  11. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  12. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
36 more
  1. 2020Convolutional Occupancy Networkscited by 761
  2. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 735
  3. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 700
  4. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  5. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  6. 2020Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humanscited by 634
  7. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  8. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 575
  9. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 449
  10. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 333
  11. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  12. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 266
  13. 2019Learning Shape Templates with Structured Implicit Functionscited by 242
  14. 2022Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstructioncited by 212
  15. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  16. 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 180
  17. 2021Improving neural implicit surfaces geometry with patch warpingcited by 137
  18. 2021NeRFReN: Neural Radiance Fields with Reflectionscited by 122
  19. 2022HF-NeuS: Improved Surface Reconstruction Using High-Frequency Detailscited by 121
  20. 2021Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objectscited by 119
  21. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 117
  22. 2019Learning to Infer Implicit Surfaces without 3D Supervisioncited by 103
  23. 2020Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networkscited by 98
  24. 2019Controlling Neural Level Setscited by 76
  25. 2023SeaThru-NeRF: Neural Radiance Fields in Scattering Mediacited by 75
  26. 2021DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Renderingcited by 52
  27. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 48
  28. 2020Event-Based Angular Velocity Regression with Spiking Networkscited by 41
  29. 2023PET-NeuS: Positional Encoding Tri-Planes for Neural Surfacescited by 37
  30. 2019A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networkscited by 36
  31. 2023Spiking PointNet: Spiking Neural Networks for Point Cloudscited by 34
  32. 2020Learning Implicit Surface Light Fieldscited by 23
  33. 2021Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Visioncited by 22
  34. 2019DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Trackingcited by 19
  35. 2022Brain-inspired Multilayer Perceptron with Spiking Neuronscited by 16
  36. 2018Adversarial Training for Probabilistic Spiking Neural Networkscited by 11

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2025Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequencescited by 5
  2. 2024Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splattingcited by 2
  3. 2024Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculationcited by 2
  4. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  5. 2024NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networkscited by 1
  6. 2025Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fieldscited by 0