SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep
Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view synthesis from continuously calibrated input views. However, conventional methods require high-frame-rate dense and high-quality sharp images, which are time-consuming and inefficient to capture, especially in dynamic environments. Event cameras, with their high temporal resolution and ability to capture asynchronous brightness changes, offer a promising alternative for more reliable scene reconstruction without motion blur. In this paper, we propose SweepEvGS, a novel hardware-integrated method that leverages event cameras for robust and accurate novel view synthesis across various imaging settings from a single sweep. SweepEvGS utilizes the initial static frame with dense event streams captured during a single camera sweep to effectively reconstruct detailed scene views. We also introduce different real-world hardware imaging systems for real-world data collection and evaluation for future research. We validate the robustness and efficiency of SweepEvGS through experiments in three different imaging settings: synthetic objects, real-world macro-level, and real-world micro-level view synthesis. Our results demonstrate that SweepEvGS surpasses existing methods in visual rendering quality, rendering speed, and computational efficiency, highlighting its potential for dynamic practical applications.
三维高斯点云技术（3D Gaussian Splatting, 3D-GS）的最新进展显示了利用三维高斯基元在高速、高保真和成本高效的新视图合成中的潜力，这基于连续校准的输入视角。然而，传统方法依赖高帧率的稠密、高质量的清晰图像，这种图像的采集在动态环境中既耗时又低效。事件相机以其高时间分辨率和捕捉异步亮度变化的能力，为无运动模糊的更可靠场景重建提供了一种有前途的替代方案。 在本文中，我们提出了 SweepEvGS，一种新型硬件集成方法，利用事件相机在各种成像条件下实现鲁棒且精确的新视图合成。SweepEvGS 使用单次相机扫描期间捕获的初始静态帧和稠密事件流，有效地重建了细节丰富的场景视图。同时，我们还介绍了不同的真实世界硬件成像系统，用于数据采集和未来研究的评估。 通过在三种不同成像条件下的实验验证了 SweepEvGS 的鲁棒性和高效性，这些条件包括合成对象、真实世界宏观视图以及真实世界微观视图合成。结果表明，SweepEvGS 在视觉渲染质量、渲染速度和计算效率上均优于现有方法，凸显了其在动态实际应用中的潜力。