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Articles

Vol. 1 No. 4 (2026)

Motion-Segmented 4D Gaussian Splatting from Unsynchronized Event-RGB Bursts

Submitted
August 6, 2026
Published
August 20, 2026

Abstract

The synthesis of highly dynamic, fast-moving scenes remains a formidable challenge in computational photography and computer vision. Traditional camera sensors suffer from severe motion blur when capturing rapid movements, while purely event-based sensors lack the dense photometric information required for photorealistic rendering. This paper introduces a novel framework for motion-segmented four-dimensional Gaussian splatting that leverages unsynchronized event-RGB bursts. By fusing the high temporal resolution of event cameras with the high spatial resolution and color fidelity of standard RGB sensors, our approach reconstructs complex dynamic scenes with unprecedented clarity. The proposed methodology addresses the critical problem of sensor asynchrony through a continuous-time optimization scheme that aligns discrete RGB exposures with continuous event streams. Furthermore, we implement a robust motion segmentation pipeline that isolates dynamic objects from static backgrounds using event density maps, allowing the four-dimensional Gaussian primitives to allocate computational resources specifically to areas of high temporal variance. Extensive evaluations across multiple challenging synthetic and real-world datasets demonstrate that our approach significantly outperforms existing dynamic scene synthesis methods in terms of rendering quality, motion blur reduction, and temporal consistency. The framework provides a crucial stepping stone for advanced applications in autonomous navigation, virtual reality, and high-speed robotic vision.

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