title: MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis
summary: Recent works in volume rendering, e.g. NeRF and 3D Gaussian
  Splatting (3DGS), significantly advance the rendering quality and efficiency
  with the help of the learned implicit neural radiance field or 3D Gaussians.
  Rendering on top of an explicit representation, the vanilla 3DGS and its
  variants deliver real-time efficiency by optimizing the parametric model with
  single-view supervision per iteration during training which is adopted from
  NeRF. Consequently, certain views are overfitted, leading to unsatisfying
  appearance in novel-view synthesis and imprecise 3D geometries. To solve
  aforementioned problems, we propose a new 3DGS optimization method embodying
  four key novel contributions: 1) We transform the conventional single-view
  training paradigm into a multi-view training strategy. With our proposed
  multi-view regulation, 3D Gaussian attributes are further optimized without
  overfitting certain training views. As a general solution, we improve the
  overall accuracy in a variety of scenarios and different Gaussian variants. 2)
  Inspired by the benefit introduced by additional views, we further propose a
  cross-intrinsic guidance scheme, leading to a coarse-to-fine training procedure
  concerning different resolutions. 3) Built on top of our multi-view regulated
  training, we further propose a cross-ray densification strategy, densifying
  more Gaussian kernels in the ray-intersect regions from a selection of views.
  4) By further investigating the densification strategy, we found that the
  effect of densification should be enhanced when certain views are distinct
  dramatically. As a solution, we propose a novel multi-view augmented
  densification strategy, where 3D Gaussians are encouraged to get densified to a
  sufficient number accordingly, resulting in improved reconstruction accuracy.
paper_authors._id: 66ff51b1804d2dcece783a74
paper_authors.name: Xiaobiao Du
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T07:31:06.175Z
paper_authors._id: 64ad086c5d48838462e2eee1
paper_authors.avatarUrl: /avatars/735b29d01bae05599e7d5ce61b223153.svg
paper_authors.fullname: Du
paper_authors.type: user
paper_authors.user: xiaobiaodu
paper_authors._id: 66ff51b1804d2dcece783a75
paper_authors.name: Yida Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:59:35.042Z
paper_authors._id: 647068944be5cf1f33491cb9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Aqudl2PTSKPAylBhlccWr.png
paper_authors.fullname: Yida Wang
paper_authors.type: user
paper_authors.user: wangyida
paper_authors._id: 66ff51b1804d2dcece783a76
paper_authors.name: Xin Yu
paper_title: MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis
paper_summary: Recent works in volume rendering, e.g. NeRF and 3D Gaussian
  Splatting (3DGS), significantly advance the rendering quality and efficiency
  with the help of the learned implicit neural radiance field or 3D Gaussians.
  Rendering on top of an explicit representation, the vanilla 3DGS and its
  variants deliver real-time efficiency by optimizing the parametric model with
  single-view supervision per iteration during training which is adopted from
  NeRF. Consequently, certain views are overfitted, leading to unsatisfying
  appearance in novel-view synthesis and imprecise 3D geometries. To solve
  aforementioned problems, we propose a new 3DGS optimization method embodying
  four key novel contributions: 1) We transform the conventional single-view
  training paradigm into a multi-view training strategy. With our proposed
  multi-view regulation, 3D Gaussian attributes are further optimized without
  overfitting certain training views. As a general solution, we improve the
  overall accuracy in a variety of scenarios and different Gaussian variants. 2)
  Inspired by the benefit introduced by additional views, we further propose a
  cross-intrinsic guidance scheme, leading to a coarse-to-fine training procedure
  concerning different resolutions. 3) Built on top of our multi-view regulated
  training, we further propose a cross-ray densification strategy, densifying
  more Gaussian kernels in the ray-intersect regions from a selection of views.
  4) By further investigating the densification strategy, we found that the
  effect of densification should be enhanced when certain views are distinct
  dramatically. As a solution, we propose a novel multi-view augmented
  densification strategy, where 3D Gaussians are encouraged to get densified to a
  sufficient number accordingly, resulting in improved reconstruction accuracy.
paper_ai_summary: A novel optimization method for 3D Gaussian Splatting improves rendering efficiency and accuracy by using multi-view training, cross-intrinsic guidance, cross-ray densification, and multi-view augmented densification strategies.
paper_ai_keywords: NeRF 3D Gaussian Splatting neural radiance field multi-view training 3D Gaussians parametric model single-view supervision novel-view synthesis multi-view regulation coarse-to-fine training cross-ray densification multi-view augmented densification