SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Radiance Field Reconstruction | Real Dataset Lion scene, 32 lux | PSNR20.37 | 10 | |
| Radiance Field Reconstruction | Real Dataset Panda scene, 17 lux | PSNR17.22 | 10 | |
| Radiance Field Reconstruction | Real Dataset House scene, 38 lux | PSNR19.29 | 10 | |
| Radiance Field Reconstruction | Real Dataset Baseball scene, 28 lux | PSNR20.25 | 10 | |
| Radiance Field Reconstruction | Real Dataset Badminton scene, 14 lux | PSNR18.84 | 10 | |
| Radiance Field Reconstruction | Real Dataset Cat scene, 16 lux | PSNR18.56 | 10 |