title: Splatfacto-W: A Nerfstudio Implementation of Gaussian Splatting for
    Unconstrained Photo Collections
summary: Novel view synthesis from unconstrained in-the-wild image collections remains
  a significant yet challenging task due to photometric variations and transient
  occluders that complicate accurate scene reconstruction. Previous methods have
  approached these issues by integrating per-image appearance features embeddings
  in Neural Radiance Fields (NeRFs). Although 3D Gaussian Splatting (3DGS) offers
  faster training and real-time rendering, adapting it for unconstrained image
  collections is non-trivial due to the substantially different architecture. In
  this paper, we introduce Splatfacto-W, an approach that integrates per-Gaussian
  neural color features and per-image appearance embeddings into the
  rasterization process, along with a spherical harmonics-based background model
  to represent varying photometric appearances and better depict backgrounds. Our
  key contributions include latent appearance modeling, efficient transient
  object handling, and precise background modeling. Splatfacto-W delivers
  high-quality, real-time novel view synthesis with improved scene consistency in
  in-the-wild scenarios. Our method improves the Peak Signal-to-Noise Ratio
  (PSNR) by an average of 5.3 dB compared to 3DGS, enhances training speed by 150
  times compared to NeRF-based methods, and achieves a similar rendering speed to
  3DGS. Additional video results and code integrated into Nerfstudio are
  available at https://kevinxu02.github.io/splatfactow/.
paper_authors._id: 66987aae2ca9a02afe5c41bd
paper_authors.name: Congrong Xu
paper_authors._id: 66987aae2ca9a02afe5c41be
paper_authors.name: Justin Kerr
paper_authors._id: 66987aae2ca9a02afe5c41bf
paper_authors.name: Angjoo Kanazawa
paper_title: Splatfacto-W: A Nerfstudio Implementation of Gaussian Splatting for
    Unconstrained Photo Collections
paper_summary: Novel view synthesis from unconstrained in-the-wild image collections remains
  a significant yet challenging task due to photometric variations and transient
  occluders that complicate accurate scene reconstruction. Previous methods have
  approached these issues by integrating per-image appearance features embeddings
  in Neural Radiance Fields (NeRFs). Although 3D Gaussian Splatting (3DGS) offers
  faster training and real-time rendering, adapting it for unconstrained image
  collections is non-trivial due to the substantially different architecture. In
  this paper, we introduce Splatfacto-W, an approach that integrates per-Gaussian
  neural color features and per-image appearance embeddings into the
  rasterization process, along with a spherical harmonics-based background model
  to represent varying photometric appearances and better depict backgrounds. Our
  key contributions include latent appearance modeling, efficient transient
  object handling, and precise background modeling. Splatfacto-W delivers
  high-quality, real-time novel view synthesis with improved scene consistency in
  in-the-wild scenarios. Our method improves the Peak Signal-to-Noise Ratio
  (PSNR) by an average of 5.3 dB compared to 3DGS, enhances training speed by 150
  times compared to NeRF-based methods, and achieves a similar rendering speed to
  3DGS. Additional video results and code integrated into Nerfstudio are
  available at https://kevinxu02.github.io/splatfactow/.
paper_ai_summary: Splatfacto-W combines Gaussian neural color features and appearance embeddings with a spherical harmonics background model for real-time, high-quality view synthesis in unconstrained image collections, improving PSNR and training efficiency.
paper_ai_keywords: Neural Radiance Fields 3D Gaussian Splatting per-Gaussian neural color features per-image appearance embeddings spherical harmonics background model latent appearance modeling transient object handling precise background modeling