  In this paper, we address common error sources for 3D Gaussian Splatting
(3DGS) including blur, imperfect camera poses, and color inconsistencies, with
the goal of improving its robustness for practical applications like
reconstructions from handheld phone captures. Our main contribution involves
modeling motion blur as a Gaussian distribution over camera poses, allowing us
to address both camera pose refinement and motion blur correction in a unified
way. Additionally, we propose mechanisms for defocus blur compensation and for
addressing color in-consistencies caused by ambient light, shadows, or due to
camera-related factors like varying white balancing settings. Our proposed
solutions integrate in a seamless way with the 3DGS formulation while
maintaining its benefits in terms of training efficiency and rendering speed.
We experimentally validate our contributions on relevant benchmark datasets
including Scannet++ and Deblur-NeRF, obtaining state-of-the-art results and
thus consistent improvements over relevant baselines.
