# SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors

arXiv: https://arxiv.org/abs/2407.03771 · HTML: https://mlsanity.com/paper/2407.03771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 25 of 25)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://mlsanity.com/paper/2310.10642/) (2023)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [GS-IR: 3D Gaussian Splatting for Inverse Rendering](https://mlsanity.com/paper/2311.16473/) (2023)
- [EventNeRF: Neural Radiance Fields from a Single Colour Event Camera](https://mlsanity.com/paper/2206.11896/) (2022)
- [GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization](https://mlsanity.com/paper/2312.05133/) (2023)
- [Ev-NeRF: Event Based Neural Radiance Field](https://mlsanity.com/paper/2206.12455/) (2022)
- [EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images](https://mlsanity.com/paper/2405.20224/) (2024)
- [Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion](https://mlsanity.com/paper/2309.08596/) (2023)
- [A Retina-inspired Sampling Method for Visual Texture Reconstruction](https://mlsanity.com/paper/1907.08769/) (2019)
- [Deformable Neural Radiance Fields using RGB and Event Cameras](https://mlsanity.com/paper/2309.08416/) (2023)
- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)
- [EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting](https://mlsanity.com/paper/2405.14959/) (2024)
- [Spike Camera and Its Coding Methods](https://mlsanity.com/paper/2104.04669/) (2021)
- [An Efficient Coding Method for Spike Camera using Inter-Spike Intervals](https://mlsanity.com/paper/1912.09669/) (2019)
- [SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream](https://mlsanity.com/paper/2403.11222/) (2024)
- [Spike-NeRF: Neural Radiance Field Based On Spike Camera](https://mlsanity.com/paper/2403.16410/) (2024)
- [SCSim: A Realistic Spike Cameras Simulator](https://mlsanity.com/paper/2405.16790/) (2024)

## Built on by (most cited first, 5 of 5)

- [USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting](https://mlsanity.com/paper/2411.10504/) (2024)
- [Splats in Splats: Robust and Effective 3D Steganography towards Gaussian Splatting](https://mlsanity.com/paper/2412.03121/) (2024)
- [Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?](https://mlsanity.com/paper/2511.22262/) (2025)
- [SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream](https://mlsanity.com/paper/2409.15176/) (2024)
- [Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography](https://mlsanity.com/paper/2604.15862/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) demonstrates unparalleled superior performance in 3D scene reconstruction. However, 3DGS heavily relies on the sharp images. Fulfilling this requirement can be challenging in real-world scenarios especially when the camera moves fast, which severely limits the application of 3DGS. To address these challenges, we proposed Spike Gausian Splatting (SpikeGS), the first framework that integrates the spike streams into 3DGS pipeline to reconstruct 3D scenes via a fast-moving bio-inspired camera. With accumulation rasterization, interval supervision, and a specially designed pipeline, SpikeGS extracts detailed geometry and texture from high temporal resolution but texture lacking spike stream, reconstructs 3D scenes captured in 1 second. Extensive experiments on multiple synthetic and real-world datasets demonstrate the superiority of SpikeGS compared with existing spike-based and deblur 3D scene reconstruction methods. Codes and data will be released soon.