title: SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
summary: Digitizing 3D static scenes and 4D dynamic events from multi-view images has
  long been a challenge in computer vision and graphics. Recently, 3D Gaussian
  Splatting (3DGS) has emerged as a practical and scalable reconstruction method,
  gaining popularity due to its impressive reconstruction quality, real-time
  rendering capabilities, and compatibility with widely used visualization tools.
  However, the method requires a substantial number of input views to achieve
  high-quality scene reconstruction, introducing a significant practical
  bottleneck. This challenge is especially severe in capturing dynamic scenes,
  where deploying an extensive camera array can be prohibitively costly. In this
  work, we identify the lack of spatial autocorrelation of splat features as one
  of the factors contributing to the suboptimal performance of the 3DGS technique
  in sparse reconstruction settings. To address the issue, we propose an
  optimization strategy that effectively regularizes splat features by modeling
  them as the outputs of a corresponding implicit neural field. This results in a
  consistent enhancement of reconstruction quality across various scenarios. Our
  approach effectively handles static and dynamic cases, as demonstrated by
  extensive testing across different setups and scene complexities.
paper_authors._id: 66ea394a6d83fd1e4fcd4483
paper_authors.name: Marko Mihajlovic
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:45:32.293Z
paper_authors._id: 62f7eddeb9fda55613cb9f03
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1660415445288-noauth.jpeg
paper_authors.fullname: Marko Mihajlovic
paper_authors.type: user
paper_authors.user: markomih
paper_authors._id: 66ea394a6d83fd1e4fcd4484
paper_authors.name: Sergey Prokudin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:45:37.796Z
paper_authors._id: 6340364b733f9eef469165ef
paper_authors.avatarUrl: /avatars/c86d79f1588bb625b97dcbe471e46def.svg
paper_authors.fullname: Sergey Prokudin
paper_authors.type: user
paper_authors.user: sprokudin
paper_authors._id: 66ea394a6d83fd1e4fcd4485
paper_authors.name: Siyu Tang
paper_authors._id: 66ea394a6d83fd1e4fcd4486
paper_authors.name: Robert Maier
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:07:29.707Z
paper_authors._id: 66eb040ee326f844d6442d56
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66eb040ee326f844d6442d56/o4Rl6tr0uiM0_hqKtkV1X.jpeg
paper_authors.fullname: Robert Maier
paper_authors.type: user
paper_authors.user: rmaier
paper_authors._id: 66ea394a6d83fd1e4fcd4487
paper_authors.name: Federica Bogo
paper_authors._id: 66ea394a6d83fd1e4fcd4488
paper_authors.name: Tony Tung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T08:46:25.576Z
paper_authors._id: 640c8783536d9fe0f001f202
paper_authors.avatarUrl: /avatars/699b9e9d3dd4ab3612b10bf2d00cebeb.svg
paper_authors.fullname: Tony Tung
paper_authors.type: user
paper_authors.user: Aeriphi
paper_authors._id: 66ea394a6d83fd1e4fcd4489
paper_authors.name: Edmond Boyer
paper_title: SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
paper_summary: Digitizing 3D static scenes and 4D dynamic events from multi-view images has
  long been a challenge in computer vision and graphics. Recently, 3D Gaussian
  Splatting (3DGS) has emerged as a practical and scalable reconstruction method,
  gaining popularity due to its impressive reconstruction quality, real-time
  rendering capabilities, and compatibility with widely used visualization tools.
  However, the method requires a substantial number of input views to achieve
  high-quality scene reconstruction, introducing a significant practical
  bottleneck. This challenge is especially severe in capturing dynamic scenes,
  where deploying an extensive camera array can be prohibitively costly. In this
  work, we identify the lack of spatial autocorrelation of splat features as one
  of the factors contributing to the suboptimal performance of the 3DGS technique
  in sparse reconstruction settings. To address the issue, we propose an
  optimization strategy that effectively regularizes splat features by modeling
  them as the outputs of a corresponding implicit neural field. This results in a
  consistent enhancement of reconstruction quality across various scenarios. Our
  approach effectively handles static and dynamic cases, as demonstrated by
  extensive testing across different setups and scene complexities.
paper_ai_summary: The proposed optimization strategy enhances 3D Gaussian Splatting performance in sparse reconstruction settings by modeling splat features using implicit neural fields.
paper_ai_keywords: 3D Gaussian Splatting 3DGS implicit neural field spatial autocorrelation reconstruction quality real-time rendering visualization tools sparsity dynamic scenes camera array optimization strategy