title: Revisiting Depth Representations for Feed-Forward 3D Gaussian Splatting
summary: Depth maps are widely used in feed-forward 3D Gaussian Splatting (3DGS)
  pipelines by unprojecting them into 3D point clouds for novel view synthesis.
  This approach offers advantages such as efficient training, the use of known
  camera poses, and accurate geometry estimation. However, depth discontinuities
  at object boundaries often lead to fragmented or sparse point clouds, degrading
  rendering quality -- a well-known limitation of depth-based representations. To
  tackle this issue, we introduce PM-Loss, a novel regularization loss based on a
  pointmap predicted by a pre-trained transformer. Although the pointmap itself
  may be less accurate than the depth map, it effectively enforces geometric
  smoothness, especially around object boundaries. With the improved depth map,
  our method significantly improves the feed-forward 3DGS across various
  architectures and scenes, delivering consistently better rendering results. Our
  project page: https://aim-uofa.github.io/PMLoss
paper_authors._id: 6842591962047f5641b3b650
paper_authors.name: Duochao Shi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:40:58.621Z
paper_authors._id: 661d1f83ea3df2195a7c2924
paper_authors.avatarUrl: /avatars/dec49fc1d79913b07b57ccbef079198f.svg
paper_authors.fullname: dcshi
paper_authors.type: user
paper_authors.user: dc-walker
paper_authors._id: 6842591962047f5641b3b651
paper_authors.name: Weijie Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:00.966Z
paper_authors._id: 66699aa8a33847217b5a49c7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png
paper_authors.fullname: Weijie Wang
paper_authors.type: user
paper_authors.user: lhmd
paper_authors._id: 6842591962047f5641b3b652
paper_authors.name: Donny Y. Chen
paper_authors._id: 6842591962047f5641b3b653
paper_authors.name: Zeyu Zhang
paper_authors._id: 6842591962047f5641b3b654
paper_authors.name: Jia-Wang Bian
paper_authors._id: 6842591962047f5641b3b655
paper_authors.name: Bohan Zhuang
paper_authors._id: 6842591962047f5641b3b656
paper_authors.name: Chunhua Shen
paper_title: Revisiting Depth Representations for Feed-Forward 3D Gaussian Splatting
paper_summary: Depth maps are widely used in feed-forward 3D Gaussian Splatting (3DGS)
  pipelines by unprojecting them into 3D point clouds for novel view synthesis.
  This approach offers advantages such as efficient training, the use of known
  camera poses, and accurate geometry estimation. However, depth discontinuities
  at object boundaries often lead to fragmented or sparse point clouds, degrading
  rendering quality -- a well-known limitation of depth-based representations. To
  tackle this issue, we introduce PM-Loss, a novel regularization loss based on a
  pointmap predicted by a pre-trained transformer. Although the pointmap itself
  may be less accurate than the depth map, it effectively enforces geometric
  smoothness, especially around object boundaries. With the improved depth map,
  our method significantly improves the feed-forward 3DGS across various
  architectures and scenes, delivering consistently better rendering results. Our
  project page: https://aim-uofa.github.io/PMLoss
paper_ai_summary: PM-Loss, a regularization technique using pointmaps from a pre-trained transformer, enhances feed-forward 3D Gaussian Splatting by improving depth map accuracy and rendering quality.
paper_ai_keywords: 3D Gaussian Splatting 3DGS depth maps point clouds novel view synthesis PM-Loss pre-trained transformer pointmap geometric smoothness