title: Robust Gaussian Splatting
summary: 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.
paper_authors._id: 6613823274f830bc7d9908ab
paper_authors.name: François Darmon
paper_authors._id: 6613823274f830bc7d9908ac
paper_authors.name: Lorenzo Porzi
paper_authors._id: 6613823274f830bc7d9908ad
paper_authors.name: Samuel Rota-Bulò
paper_authors._id: 6613823274f830bc7d9908ae
paper_authors.name: Peter Kontschieder
paper_title: Robust Gaussian Splatting
paper_summary: 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.
paper_ai_keywords: 3D Gaussian Splatting 3DGS motion blur Gaussian distribution camera pose refinement defocus blur compensation color inconsistencies ambient light shadows white balancing settings Scannet++ Deblur-NeRF