title: CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction
    Model
summary: Feed-forward 3D generative models like the Large Reconstruction Model (LRM)
  have demonstrated exceptional generation speed. However, the transformer-based
  methods do not leverage the geometric priors of the triplane component in their
  architecture, often leading to sub-optimal quality given the limited size of 3D
  data and slow training. In this work, we present the Convolutional
  Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D
  generative model. Recognizing the limitations posed by sparse 3D data, we
  highlight the necessity of integrating geometric priors into network design.
  CRM builds on the key observation that the visualization of triplane exhibits
  spatial correspondence of six orthographic images. First, it generates six
  orthographic view images from a single input image, then feeds these images
  into a convolutional U-Net, leveraging its strong pixel-level alignment
  capabilities and significant bandwidth to create a high-resolution triplane.
  CRM further employs Flexicubes as geometric representation, facilitating direct
  end-to-end optimization on textured meshes. Overall, our model delivers a
  high-fidelity textured mesh from an image in just 10 seconds, without any
  test-time optimization.
paper_authors._id: 65ee693554082af7dbd87ee1
paper_authors.name: Zhengyi Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: Zhengyi Wang
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paper_authors.user: Zhengyi
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paper_authors.name: Yikai Wang
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paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiw
paper_authors._id: 65ee693554082af7dbd87ee3
paper_authors.name: Yifei Chen
paper_authors._id: 65ee693554082af7dbd87ee4
paper_authors.name: Chendong Xiang
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paper_authors.fullname: Xiangchendong
paper_authors.type: user
paper_authors.user: Xiang-cd
paper_authors._id: 65ee693554082af7dbd87ee5
paper_authors.name: Shuo Chen
paper_authors.status: claimed_verified
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paper_authors.fullname: ShuoChen
paper_authors.type: user
paper_authors.user: ShuoChen20
paper_authors._id: 65ee693554082af7dbd87ee6
paper_authors.name: Dajiang Yu
paper_authors._id: 65ee693554082af7dbd87ee7
paper_authors.name: Chongxuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-11T09:06:13.667Z
paper_authors._id: 64c07b488e2612254361153b
paper_authors.avatarUrl: /avatars/ade0f783cc4c2d3e73f402637f595471.svg
paper_authors.fullname: chongxuan li
paper_authors.type: user
paper_authors.user: zhenxuan00
paper_authors._id: 65ee693554082af7dbd87ee8
paper_authors.name: Hang Su
paper_authors._id: 65ee693554082af7dbd87ee9
paper_authors.name: Jun Zhu
paper_title: CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction
    Model
paper_summary: Feed-forward 3D generative models like the Large Reconstruction Model (LRM)
  have demonstrated exceptional generation speed. However, the transformer-based
  methods do not leverage the geometric priors of the triplane component in their
  architecture, often leading to sub-optimal quality given the limited size of 3D
  data and slow training. In this work, we present the Convolutional
  Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D
  generative model. Recognizing the limitations posed by sparse 3D data, we
  highlight the necessity of integrating geometric priors into network design.
  CRM builds on the key observation that the visualization of triplane exhibits
  spatial correspondence of six orthographic images. First, it generates six
  orthographic view images from a single input image, then feeds these images
  into a convolutional U-Net, leveraging its strong pixel-level alignment
  capabilities and significant bandwidth to create a high-resolution triplane.
  CRM further employs Flexicubes as geometric representation, facilitating direct
  end-to-end optimization on textured meshes. Overall, our model delivers a
  high-fidelity textured mesh from an image in just 10 seconds, without any
  test-time optimization.
paper_ai_summary: A convolutional generative model that integrates geometric priors and leverages Flexicubes for efficient high-fidelity 3D mesh generation from a single image.
paper_ai_keywords: feed-forward 3D generative models Large Reconstruction Model (LRM) transformer-based methods geometric priors triplane component Convolutional Reconstruction Model (CRM) convolutional U-Net pixel-level alignment textured meshes Flexicubes