title: MeshLRM: Large Reconstruction Model for High-Quality Mesh
summary: We propose MeshLRM, a novel LRM-based approach that can reconstruct a
  high-quality mesh from merely four input images in less than one second.
  Different from previous large reconstruction models (LRMs) that focus on
  NeRF-based reconstruction, MeshLRM incorporates differentiable mesh extraction
  and rendering within the LRM framework. This allows for end-to-end mesh
  reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering.
  Moreover, we improve the LRM architecture by simplifying several complex
  designs in previous LRMs. MeshLRM's NeRF initialization is sequentially trained
  with low- and high-resolution images; this new LRM training strategy enables
  significantly faster convergence and thereby leads to better quality with less
  compute. Our approach achieves state-of-the-art mesh reconstruction from
  sparse-view inputs and also allows for many downstream applications, including
  text-to-3D and single-image-to-3D generation. Project page:
  https://sarahweiii.github.io/meshlrm/
paper_authors._id: 6621f53711047ec8884147b2
paper_authors.name: Xinyue Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-19T08:28:25.090Z
paper_authors._id: 646d6615da8e99940b7143cf
paper_authors.avatarUrl: /avatars/e6e24e993fc4f3e9c7026b4c34bd1a25.svg
paper_authors.fullname: Xinyue Wei
paper_authors.type: user
paper_authors.user: sarahwei0210
paper_authors._id: 6621f53711047ec8884147b3
paper_authors.name: Kai Zhang
paper_authors._id: 6621f53711047ec8884147b4
paper_authors.name: Sai Bi
paper_authors._id: 6621f53711047ec8884147b5
paper_authors.name: Hao Tan
paper_authors._id: 6621f53711047ec8884147b6
paper_authors.name: Fujun Luan
paper_authors._id: 6621f53711047ec8884147b7
paper_authors.name: Valentin Deschaintre
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-19T08:30:04.563Z
paper_authors._id: 63e0dc30ab3fb81c39816ce7
paper_authors.avatarUrl: /avatars/a30c8ca8850ae120dd409b6f7d204493.svg
paper_authors.fullname: Valentin Deschaintre
paper_authors.type: user
paper_authors.user: deschain
paper_authors._id: 6621f53711047ec8884147b8
paper_authors.name: Kalyan Sunkavalli
paper_authors._id: 6621f53711047ec8884147b9
paper_authors.name: Hao Su
paper_authors._id: 6621f53711047ec8884147ba
paper_authors.name: Zexiang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-19T08:30:15.052Z
paper_authors._id: 65565b1b8616d8d740abb976
paper_authors.avatarUrl: /avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
paper_authors.fullname: Zexiang Xu
paper_authors.type: user
paper_authors.user: zexiangxu
paper_title: MeshLRM: Large Reconstruction Model for High-Quality Mesh
paper_summary: We propose MeshLRM, a novel LRM-based approach that can reconstruct a
  high-quality mesh from merely four input images in less than one second.
  Different from previous large reconstruction models (LRMs) that focus on
  NeRF-based reconstruction, MeshLRM incorporates differentiable mesh extraction
  and rendering within the LRM framework. This allows for end-to-end mesh
  reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering.
  Moreover, we improve the LRM architecture by simplifying several complex
  designs in previous LRMs. MeshLRM's NeRF initialization is sequentially trained
  with low- and high-resolution images; this new LRM training strategy enables
  significantly faster convergence and thereby leads to better quality with less
  compute. Our approach achieves state-of-the-art mesh reconstruction from
  sparse-view inputs and also allows for many downstream applications, including
  text-to-3D and single-image-to-3D generation. Project page:
  https://sarahweiii.github.io/meshlrm/
paper_ai_summary: MeshLRM, an LRM-based approach, reconstructs high-quality meshes efficiently from four images using differentiable mesh extraction and rendering, enhancing NeRF-based methods.
paper_ai_keywords: LRM MeshLRM differentiable mesh extraction rendering NeRF end-to-end mesh reconstruction NeRF LRM sequential training low-resolution images high-resolution images faster convergence state-of-the-art mesh reconstruction text-to-3D single-image-to-3D generation