Evagaussians: Event Stream Assisted Gaussian Splatting from Blurry Images

3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in synthesizing novel views of 3D scenes. However, its training is heavily reliant on high-quality images and precise camera poses. Meeting these criteria can be challenging in non-ideal real-world conditions, where motion-blurred images frequently occur due to high-speed camera movements or low-light environments. To address these challenges, we introduce Event Stream Assisted Gaussian Splatting (EvaGaussians), a novel approach that harnesses event streams captured by event cameras to facilitate the learning of high-quality 3D-GS from blurred images. Capitalizing on the high temporal resolution and dynamic range offered by event streams, we seamlessly integrate them into the initialization and optimization of 3D-GS, thereby enhancing the acquisition of high-fidelity novel views with intricate texture details. To remedy the absence of evaluation benchmarks incorporating both event streams and RGB frames, we present two novel datasets comprising RGB frames, event streams, and corresponding camera parameters, featuring a wide variety of scenes and various camera motions. We then conduct a thorough evaluation of our method, comparing it with leading techniques on the provided benchmark. The comparison results reveal that our approach not only excels in generating high-fidelity novel views, but also offers faster training and inference speeds. Video results are available at the project page.