3D Gaussian Splatting from Unposed Spike Stream

3D Gaussian Splatting (3DGS) has significantly advanced 3D reconstruction with its impressive performance. However, its reliance on sharp images and precise camera pose priors limits its effectiveness in high-speed scenarios. Recent advances have integrated spike camera, a bio-inspired sensor with a high temporal resolution, to enhance 3DGS in such conditions. Although spike-based methods reduce the need for sharp images, they still face challenges in achieving precise camera pose estimation due to unstable observations and visual texture deficiency.To address these challenges, we propose Nope-SGS, the first framework that reconstructs high-speed 3D scenes from unposed captures of the bio-inspired high-temporal-resolution spike camera. To achieve robust 3D reconstruction and pose estimation, we first reformulate the spike model from a probabilistic perspective and extend its application to keyframing, effectively alleviating the instability caused by the spike stream. Building upon this foundation, we devise a progressive optimization framework to facilitate swift 3D reconstruction. The experimental results demonstratethat our method achieves up to 7.4dB higher PSNR and 40\% lower AbsoluteTrajectory Error (ATE) compared to state-of-the-art methodsunder challenging high-speed scenarios while maintaining the fastest reconstruction speed among spike-based methods.