MAD-GS:3D Gaussian Splatting for Motion and Defocus Images in Robotic Vision

Abstract

Recent advancements in 3D Gaussian Splatting (3DGS) have significantly improved novel view synthesis, playing a crucial role in robotic vision and scene reconstruction. However, 3DGS relies heavily on precise camera poses and sharp images, which are often difficult to obtain in real-world robotic applications due to motion and defocus blur. Directly applying 3DGS to blurred images results in severe degradation, limiting its effectiveness in tasks such as autonomous navigation and manipulation. To address this challenge, we propose MAD-GS, a novel deblurring framework based on 3DGS, specifically designed for robotic vision tasks. MAD-GS effectively mitigates motion and defocus blur while refining imprecise camera poses, enhancing 3D scene reconstruction under real-world uncertainties. Additionally, we introduce a blur segmentation mask to identify and adaptively refine heavily blurred regions, improving visual quality and downstream robotic decision-making. Extensive experiments on synthetic and real-world datasets demonstrate that MAD-GS outperforms existing methods, leading to superior image clarity and fidelity, thereby advancing robust robot perception in dynamic environments.