Event-Based 3D Gaussian Splatting (3DGS)

• Event-based 3D Gaussian Splatting is a method that represents scenes using anisotropic 3D Gaussian primitives, enabling photorealistic and motion-robust rendering.
• It utilizes high-temporal event camera data and differentiable rendering to achieve real-time novel view synthesis and orders-of-magnitude speedups over NeRF-style techniques.
• The approach is scalable across static, dynamic, and large-scale environments and can be extended with pose refinement and exposure event integration for enhanced low-light performance.

Event-Based 3D Gaussian Splatting (3DGS) is an explicit scene representation and rendering methodology that leverages high-frequency, asynchronous data from event cameras for photorealistic, efficient, and motion-robust volumetric rendering and 3D reconstruction. By directly supervising anisotropic 3D Gaussian primitives using event-based supervisory signals—eschewing reliance on conventional RGB frames—these methods achieve real-time novel view synthesis with inherent immunity to motion blur and extreme lighting conditions. Event-based 3DGS is now demonstrated across static, dynamic, and large-scale scenes, forming a new regime of high-speed, high-fidelity radiance field rendering and 3D vision that is orders of magnitude faster than NeRF-like architectures.

1. Foundations: 3D Gaussian Primitive Parameterization

Event-based 3DGS represents a scene as a set of $M$ explicit spatial primitives—anisotropic 3D Gaussians (“splats”)—each parameterized by:

• Center $\boldsymbol{\mu}_i \in \mathbb{R}^3$
• Covariance matrix $\Sigma_i \succ 0$ ($3\times3$)
• RGB color or feature vector $\mathbf{c}_i \in \mathbb{R}^3$ (optionally learned per view, often via spherical harmonics)
• Opacity (density) $\alpha_i \in [0,1]$

The spatial contribution of Gaussian $G_i$ at point $\mathbf{x} \in \mathbb{R}^3$ is modeled as: $$G_i(\mathbf{x}) = \alpha_i \exp \left[ -\frac{1}{2} (\mathbf{x}-\boldsymbol{\mu}_i)^\top \Sigma_i^{-1} (\mathbf{x}-\boldsymbol{\mu}_i) \right] \mathbf{c}_i$$

Projection into image space is performed by transforming $\boldsymbol{\mu}_i, \Sigma_i$ under current camera pose; the Gaussian is rasterized as a 2D elliptical kernel $$\mathcal{N}(\mathbf{u};\boldsymbol{\mu}'_i,\Sigma'_i)$$ where $\mathbf{u}$ is a pixel, and accumulation across all splats and alpha-composited colors forms the rendered radiance field. The compositing can be written as: $$I(\mathbf{u}) = \sum_{i=1}^M w_i(\mathbf{u}) \mathbf{c}_i \prod_{j<i} (1 - w_j(\mathbf{u}))$$ with $w_i(\mathbf{u})$ defined by the splat’s image-space parameters.

This explicit, analytic structure allows for fully differentiable optimization and avoids expensive per-ray neural field inference, yielding dramatic speedups in both training and real-time rendering (Wu et al., 16 Jul 2024, Xiong et al., 5 Jun 2024, Zahid et al., 15 Feb 2025).

2. Event Camera Measurement and Supervision Model

An event camera produces an asynchronous stream of events $e_k=(x_k,y_k,t_k,p_k)$ whenever the change in log-intensity $L$ at pixel $(x_k, y_k)$ and time $t_k$ surpasses a contrast threshold $A$: $$\Delta L_k = L(x_k, y_k, t_k) - L(x_k, y_k, t_{k-1}) = p_k A, \quad p_k \in \{+1, -1\}$$

Rather than observing dense RGB frames, event-based 3DGS uses these events either as:

• Accumulated event maps over time windows as direct supervision of log-intensity change between synthesized views (“event frame loss”) (Wu et al., 16 Jul 2024, Yura et al., 10 Dec 2024, Zahid et al., 15 Feb 2025), or
• Raw event streams to directly supervise the log-intensity increments at event timestamps (Yin et al., 22 Oct 2024).

For dynamic scenes, some methods exploit the temporal profile of events for scene flow, motion prior, and deformation field supervision (He et al., 9 Oct 2025).

This event stream, being sparse but exceptionally high-temporal-resolution, naturally avoids motion blur and extreme illumination issues, rendering event-based 3DGS highly robust in high-speed and difficult lighting scenarios (Wu et al., 16 Jul 2024, Zahid et al., 15 Feb 2025).

3. Differentiable Rendering and Loss Formulation

The central loss in event-based 3D Gaussian Splatting minimizes the discrepancy between ground-truth aggregated event images $E_\text{gt}$ and synthetic event accumulation $E_\text{pred}$ derived from time-separated rendered Gaussian fields: $E_\text{pred}(u) = L(I_t(u)) - L(I_{t-w}(u))$

$$E_\text{gt}(u) = \sum_{k \: : \: (x_k, y_k) = u, \; t_{k-1} \geq t-w} p_k$$

The event-based loss typically comprises a per-pixel (often normalized) $\ell_2$ term, optionally passed through a “linlog” nonlinearity for numerical stability and log-domain consistency, plus a weak structural similarity (SSIM) component for gradient regularization: $$L_\text{event} = \|\text{linlog}(E_\text{pred}) - \text{linlog}(E_\text{gt})\|_2^2 + \lambda(1 - \text{SSIM}(E_\text{pred}, E_\text{gt}))$$

For high-fidelity texture or deformation modeling, additional terms may include photometric losses (if RGB or exposure events are available) and flow/deformation constraints for dynamic scenes (He et al., 9 Oct 2025, Liao et al., 20 Oct 2024).

All Gaussian parameters ($$\boldsymbol{\mu}_i, \Sigma_i, \mathbf{c}_i, \alpha_i$$) are updated via backpropagation through a differentiable rasterizer (Wu et al., 16 Jul 2024, Yura et al., 10 Dec 2024, Zahid et al., 15 Feb 2025).

4. Scene Initialization, Pose Representation, and Scalability

A robust initialization is critical to ensure stable optimization:

• For static scenes, initial Gaussians may be seeded from sparse SfM on pseudo-frames reconstructed with E2V models, COLMAP, or random sampling within observed frusta (Yura et al., 10 Dec 2024, Zahid et al., 15 Feb 2025).
• For event-only settings, edge-based IWE (Image of Warped Events) maps optionally guide the placement of the initial cloud (Kohyama et al., 21 Dec 2025).

Camera pose interpolation for microsecond-scale temporal alignment employs either cubic splines or Bézier SE(3) trajectories, enabling accurate pose assignment per event or synthetic frame (Yura et al., 10 Dec 2024, Matta et al., 26 Dec 2024).

For large-scale and unbounded scenes, explicit frustum-based placement of tens of thousands of Gaussians is used per subvolume or view, followed by progressive densification/pruning during optimization (Zahid et al., 15 Feb 2025), yielding datasets and pipelines capable of operating over “city-block” ranges and tens of millions of events.

5. Efficiency, Empirical Performance, and Comparison to Neural Fields

Event-based 3DGS delivers exceptional computational efficiency versus NeRF-derived event rendering. Representative figures (Wu et al., 16 Jul 2024, Xiong et al., 5 Jun 2024, Zahid et al., 15 Feb 2025, Yura et al., 10 Dec 2024):

• Training time: tens of minutes (e.g., 9–12 min) versus hours to days for NeRF (e.g., 14 h), up to $80\times$–$100\times$ faster.
• Real-time rendering: 53–140 fps (vs. $<1$ fps for NeRF-based models).
• Memory: $\sim$5 GB (vs. 15 GB for neural-field ray marching).
• Achieved PSNR up to 42 dB (DEGS, dynamic scenes), 28–32 dB (Ev-GS, E-3DGS, EventSplat) in static or large-scale scenes, with SSIM up to 0.97.
• Robustness: strong performance under extreme motion, low contrast, and low-light, outperforming or matching both frame-based and event-NeRF methods.

A typical comparison table for static scenes (Wu et al., 16 Jul 2024, Yura et al., 10 Dec 2024):

Method	PSNR (dB)	SSIM	Training time	Rendering FPS	Memory (GB)
EventNeRF	25–28	0.91	14 h	0.3	15
Ev-GS	28–28.1	0.93	9–12 min	53–66	5
EventSplat	28.1	0.95	2 h	200	5
E-3DGS	29–42	0.97	1–2 h	65+	5

Event-based 3DGS consistently demonstrates state-of-the-art accuracy and speed for both static and highly dynamic or large-scale settings (Zahid et al., 15 Feb 2025, He et al., 9 Oct 2025, Yura et al., 10 Dec 2024).

6. Extensions: Dynamic Scenes, Pose Refinement, Exposure Events

Recent work extends event-based 3DGS beyond static and rigid environments:

• Dynamic/non-rigid scenes leverage event-supervised motion priors to optimize deformation fields via geometry-aware, flow-guided supervision, typically with an MLP-parameterized per-Gaussian deformation model (He et al., 9 Oct 2025). Event flows calibrate inter-frame trajectories and refine both geometry and appearance, producing significant PSNR gains over prior methods.
• Pose refinement and free-trajectory capture use event-driven contrast maximization (via IWE or CMax objectives) to jointly optimize camera motion and Gaussian parameters, reducing drift especially under sparse or high-speed captures (Liao et al., 20 Oct 2024).
• Exposure events, enabled by hardware-modified event cameras (with programmable transmittance modulation), allow single-shot high-fidelity grayscale images for dense geometry supervision, further enhancing quality under low-light or high dynamic range (Yin et al., 22 Oct 2024).
• Hardware-integrated pipelines support real-world industrial or biomedical scanning, including turntable and microscope setups, with explicit, single-sweep event-based 3DGS reconstruction (Wu et al., 16 Dec 2024).

7. Unique Capabilities, Limitations, and Prospects

Event-based 3D Gaussian Splatting establishes the first real-time, motion-robust, and frame-free approach for dense 3D reconstruction and synthesizing novel radiance fields under challenging conditions. Key attributes include:

• Immunity to motion blur and high dynamic range due to microsecond event quantization (Wu et al., 16 Jul 2024, Yura et al., 10 Dec 2024, Zahid et al., 15 Feb 2025).
• Orders-of-magnitude speedup in both training and inference compared to neural implicit models (Wu et al., 16 Jul 2024, Xiong et al., 5 Jun 2024).
• End-to-end differentiable frameworks suitable for static, dynamic, large-scale, and hardware-accelerated scenarios (He et al., 9 Oct 2025, Zahid et al., 15 Feb 2025, Wu et al., 16 Dec 2024).
• Extension to dynamic scenes and flow/deformation learning using coupled RGB and event modalities (He et al., 9 Oct 2025, Liao et al., 20 Oct 2024).

Current limitations involve handling extremely sparse or noisy event streams, modeling textureless planar surfaces, and extending to arbitrary hand-held or unconstrained motion without explicit pose sensors. Future directions include more sophisticated event initialization, integration with exposure event hardware, adaptive Gaussian covariance priors, and joint pose-geometry refinement.

Event-based 3DGS represents a significant convergence of neuromorphic sensing, explicit volumetric rendering, and differentiable computer vision, setting new standards in speed, robustness, and fidelity for event-driven 3D scene understanding (Wu et al., 16 Jul 2024, Xiong et al., 5 Jun 2024, Yura et al., 10 Dec 2024, Zahid et al., 15 Feb 2025, He et al., 9 Oct 2025, Wu et al., 16 Dec 2024, Yin et al., 22 Oct 2024, Liao et al., 20 Oct 2024).