ERF-GS: Reconstructing Fast Motion from Disjoint Event-RGB Viewpoints
Deep learning-driven representations such as neural radiance fields (NeRFs) and 3D Gaussian splatting (3DGS) have revolutionized the field of dynamic 3D scene reconstruction with improved visual precision and scalability. However, the reconstruction of fast-moving objects remains a challenge; existing methods based on conventional frame-based videos often struggle in scenarios such as sports events and animal videography. We propose an event-RGB fusion Gaussian splatting (ERF-GS) framework that integrates event information into both optimization and densification stages of the Gaussian splatting pipeline, taking advantage of novel event sensors with high frame-rate. Unlike many other even...
| Authors | Xiaoyang Bai, Zhenyang Li, Weiwei Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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