E2GS: Event Enhanced Gaussian Splatting
This work addresses the need for efficient 3D reconstruction in applications like robotics or AR/VR, though it is incremental as it builds on existing Gaussian Splatting methods.
The paper tackles the problem of slow training and rendering in event-based 3D reconstruction by introducing E2GS, which integrates event data into Gaussian Splatting, achieving high-quality novel view synthesis and faster speeds of 140 FPS.
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 https://github.com/deguchihiroyuki/E2GS.