CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion-Blurred Images
This addresses the challenge of motion blur in 3D Gaussian Splatting for real-world applications, representing a strong specific gain in the domain.
The paper tackles the problem of camera motion blur hindering accurate 3D scene reconstruction by proposing CRiM-GS, a method that reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed, achieving state-of-the-art performance on benchmark datasets.
3D Gaussian Splatting (3DGS) has gained significant attention for their high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction. In this study, we propose CRiM-GS, a \textbf{C}ontinuous \textbf{Ri}gid \textbf{M}otion-aware \textbf{G}aussian \textbf{S}platting that reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed. Considering the complex motion patterns inherent in real-world camera movements, we predict continuous camera trajectories using neural ordinary differential equations (ODE). To ensure accurate modeling, we employ rigid body transformations with proper regularization, preserving object shape and size. Additionally, we introduce an adaptive distortion-aware transformation to compensate for potential nonlinear distortions, such as rolling shutter effects, and unpredictable camera movements. By revisiting fundamental camera theory and leveraging advanced neural training techniques, we achieve precise modeling of continuous camera trajectories. Extensive experiments demonstrate state-of-the-art performance both quantitatively and qualitatively on benchmark datasets.