title: DiMeR: Disentangled Mesh Reconstruction Model
summary: With the advent of large-scale 3D datasets, feed-forward 3D generative
  models, such as the Large Reconstruction Model (LRM), have gained significant
  attention and achieved remarkable success. However, we observe that RGB images
  often lead to conflicting training objectives and lack the necessary clarity
  for geometry reconstruction. In this paper, we revisit the inductive biases
  associated with mesh reconstruction and introduce DiMeR, a novel disentangled
  dual-stream feed-forward model for sparse-view mesh reconstruction. The key
  idea is to disentangle both the input and framework into geometry and texture
  parts, thereby reducing the training difficulty for each part according to the
  Principle of Occam's Razor. Given that normal maps are strictly consistent with
  geometry and accurately capture surface variations, we utilize normal maps as
  exclusive input for the geometry branch to reduce the complexity between the
  network's input and output. Moreover, we improve the mesh extraction algorithm
  to introduce 3D ground truth supervision. As for texture branch, we use RGB
  images as input to obtain the textured mesh. Overall, DiMeR demonstrates robust
  capabilities across various tasks, including sparse-view reconstruction,
  single-image-to-3D, and text-to-3D. Numerous experiments show that DiMeR
  significantly outperforms previous methods, achieving over 30% improvement in
  Chamfer Distance on the GSO and OmniObject3D dataset.
paper_authors._id: 680af5c5e5f0b6a3068bc842
paper_authors.name: Lutao Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T08:34:32.497Z
paper_authors._id: 65d61d8fd484956b5acc89fe
paper_authors.avatarUrl: /avatars/47954232c90780ffe898a5a445f7fb0a.svg
paper_authors.fullname: Lutao Jiang
paper_authors.type: user
paper_authors.user: LutaoJiang
paper_authors._id: 680af5c5e5f0b6a3068bc843
paper_authors.name: Jiantao Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-26T08:53:36.695Z
paper_authors._id: 6332e2689bf698ce68a22e8c
paper_authors.avatarUrl: /avatars/c1922acfda2e6d2fe7b03194a404eb10.svg
paper_authors.fullname: JIANTAO LIN
paper_authors.type: user
paper_authors.user: LTT
paper_authors._id: 680af5c5e5f0b6a3068bc844
paper_authors.name: Kanghao Chen
paper_authors._id: 680af5c5e5f0b6a3068bc845
paper_authors.name: Wenhang Ge
paper_authors._id: 680af5c5e5f0b6a3068bc846
paper_authors.name: Xin Yang
paper_authors._id: 680af5c5e5f0b6a3068bc847
paper_authors.name: Yifan Jiang
paper_authors._id: 680af5c5e5f0b6a3068bc848
paper_authors.name: Yuanhuiyi Lyu
paper_authors._id: 680af5c5e5f0b6a3068bc849
paper_authors.name: Xu Zheng
paper_authors._id: 680af5c5e5f0b6a3068bc84a
paper_authors.name: Yingcong Chen
paper_title: DiMeR: Disentangled Mesh Reconstruction Model
paper_summary: With the advent of large-scale 3D datasets, feed-forward 3D generative
  models, such as the Large Reconstruction Model (LRM), have gained significant
  attention and achieved remarkable success. However, we observe that RGB images
  often lead to conflicting training objectives and lack the necessary clarity
  for geometry reconstruction. In this paper, we revisit the inductive biases
  associated with mesh reconstruction and introduce DiMeR, a novel disentangled
  dual-stream feed-forward model for sparse-view mesh reconstruction. The key
  idea is to disentangle both the input and framework into geometry and texture
  parts, thereby reducing the training difficulty for each part according to the
  Principle of Occam's Razor. Given that normal maps are strictly consistent with
  geometry and accurately capture surface variations, we utilize normal maps as
  exclusive input for the geometry branch to reduce the complexity between the
  network's input and output. Moreover, we improve the mesh extraction algorithm
  to introduce 3D ground truth supervision. As for texture branch, we use RGB
  images as input to obtain the textured mesh. Overall, DiMeR demonstrates robust
  capabilities across various tasks, including sparse-view reconstruction,
  single-image-to-3D, and text-to-3D. Numerous experiments show that DiMeR
  significantly outperforms previous methods, achieving over 30% improvement in
  Chamfer Distance on the GSO and OmniObject3D dataset.
paper_ai_summary: DiMeR, a disentangled dual-stream feed-forward model, improves sparse-view 3D mesh reconstruction using normal maps for geometry and RGB images for texture, outperforming previous methods by over 30% in Chamfer Distance.
paper_ai_keywords: feed-forward 3D generative models Large Reconstruction Model (LRM) mesh reconstruction disentangled dual-stream geometry branch texture branch normal maps 3D ground truth supervision Chamfer Distance GSO OmniObject3D dataset