E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras

2025-08-13

AI Summary

Existing RGB-based novel view synthesis and 4D reconstruction methods suffer from limited performance under low-light conditions and high-speed motion—due to illumination dependency, motion blur, and narrow dynamic range. To address this, we propose the first event-driven dynamic Gaussian splatting framework. Our method introduces event-stream-guided 3D initialization, adaptive temporal slicing for point cloud construction, and intensity-aware importance pruning to enhance temporal modeling fidelity and 3D consistency. Furthermore, it integrates multi-view event alignment, contrast-adaptive threshold optimization, and native event-based rendering for end-to-end high-fidelity dynamic scene reconstruction. Evaluated on a newly constructed multi-view event dataset, our approach significantly outperforms state-of-the-art pure-event and event-RGB fusion methods, particularly in challenging high-speed motion and low-light scenarios—achieving substantial gains in reconstruction accuracy and geometric fidelity.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Novel view synthesis and 4D reconstruction techniques predominantly rely on RGB cameras, thereby inheriting inherent limitations such as the dependence on adequate lighting, susceptibility to motion blur, and a limited dynamic range. Event cameras, offering advantages of low power, high temporal resolution and high dynamic range, have brought a new perspective to addressing the scene reconstruction challenges in high-speed motion and low-light scenes. To this end, we propose E-4DGS, the first event-driven dynamic Gaussian Splatting approach, for novel view synthesis from multi-view event streams with fast-moving cameras. Specifically, we introduce an event-based initialization scheme to ensure stable training and propose event-adaptive slicing splatting for time-aware reconstruction. Additionally, we employ intensity importance pruning to eliminate floating artifacts and enhance 3D consistency, while incorporating an adaptive contrast threshold for more precise optimization. We design a synthetic multi-view camera setup with six moving event cameras surrounding the object in a 360-degree configuration and provide a benchmark multi-view event stream dataset that captures challenging motion scenarios. Our approach outperforms both event-only and event-RGB fusion baselines and paves the way for the exploration of multi-view event-based reconstruction as a novel approach for rapid scene capture.

Problem

• Dynamic scene reconstruction from multi-view event cameras
• Overcoming RGB camera limitations in high-speed motion
• Novel view synthesis in low-light and fast-moving scenarios

Innovation

• Event-driven dynamic Gaussian Splatting approach
• Event-based initialization for stable training
• Intensity importance pruning for artifact elimination