Event cameras, known for their high dynamic range, absence of motion blur,
and low energy usage, have recently found a wide range of applications thanks
to these attributes. In the past few years, the field of event-based 3D
reconstruction saw remarkable progress, with the Neural Radiance Field (NeRF)
based approach demonstrating photorealistic view synthesis results. However,
the volume rendering paradigm of NeRF necessitates extensive training and
rendering times. In this paper, we introduce Event Enhanced Gaussian Splatting
(E2GS), a novel method that incorporates event data into Gaussian Splatting,
which has recently made significant advances in the field of novel view
synthesis. Our E2GS effectively utilizes both blurry images and event data,
significantly improving image deblurring and producing high-quality novel view
synthesis. Our comprehensive experiments on both synthetic and real-world
datasets demonstrate our E2GS can generate visually appealing renderings while
offering faster training and rendering speed (140 FPS). Our code is available
at this https URL

使用 Event Enhanced Gaussian Splatting（E2GS）方法，结合事件数据和高斯分层方法，以提高图像去模糊和产生高质量的新视角合成，并在综合实验中展示了较快训练和渲染速度（140 FPS）