title: SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single
    Images
summary: We study the problem of single-image 3D object reconstruction. Recent works
  have diverged into two directions: regression-based modeling and generative
  modeling. Regression methods efficiently infer visible surfaces, but struggle
  with occluded regions. Generative methods handle uncertain regions better by
  modeling distributions, but are computationally expensive and the generation is
  often misaligned with visible surfaces. In this paper, we present SPAR3D, a
  novel two-stage approach aiming to take the best of both directions. The first
  stage of SPAR3D generates sparse 3D point clouds using a lightweight point
  diffusion model, which has a fast sampling speed. The second stage uses both
  the sampled point cloud and the input image to create highly detailed meshes.
  Our two-stage design enables probabilistic modeling of the ill-posed
  single-image 3D task while maintaining high computational efficiency and great
  output fidelity. Using point clouds as an intermediate representation further
  allows for interactive user edits. Evaluated on diverse datasets, SPAR3D
  demonstrates superior performance over previous state-of-the-art methods, at an
  inference speed of 0.7 seconds. Project page with code and model:
  https://spar3d.github.io
paper_authors._id: 677f73632fcceb4c315524de
paper_authors.name: Zixuan Huang
paper_authors._id: 677f73632fcceb4c315524df
paper_authors.name: Mark Boss
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-09T10:06:00.640Z
paper_authors._id: 64b7f06fda8017900e893eb4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b7f06fda8017900e893eb4/3VcQFjERXyAjHQFLMFEJt.jpeg
paper_authors.fullname: Mark Boss
paper_authors.type: user
paper_authors.user: mboss
paper_authors._id: 677f73632fcceb4c315524e0
paper_authors.name: Aaryaman Vasishta
paper_authors._id: 677f73632fcceb4c315524e1
paper_authors.name: James M. Rehg
paper_authors._id: 677f73632fcceb4c315524e2
paper_authors.name: Varun Jampani
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:30:32.287Z
paper_authors._id: 630e7cfe3fc17ffc50f6e835
paper_authors.avatarUrl: /avatars/0b742ff094a09f9374fafcd97ab9e002.svg
paper_authors.fullname: Varun Jampani
paper_authors.type: user
paper_authors.user: varunjampani
paper_title: SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single
    Images
paper_summary: We study the problem of single-image 3D object reconstruction. Recent works
  have diverged into two directions: regression-based modeling and generative
  modeling. Regression methods efficiently infer visible surfaces, but struggle
  with occluded regions. Generative methods handle uncertain regions better by
  modeling distributions, but are computationally expensive and the generation is
  often misaligned with visible surfaces. In this paper, we present SPAR3D, a
  novel two-stage approach aiming to take the best of both directions. The first
  stage of SPAR3D generates sparse 3D point clouds using a lightweight point
  diffusion model, which has a fast sampling speed. The second stage uses both
  the sampled point cloud and the input image to create highly detailed meshes.
  Our two-stage design enables probabilistic modeling of the ill-posed
  single-image 3D task while maintaining high computational efficiency and great
  output fidelity. Using point clouds as an intermediate representation further
  allows for interactive user edits. Evaluated on diverse datasets, SPAR3D
  demonstrates superior performance over previous state-of-the-art methods, at an
  inference speed of 0.7 seconds. Project page with code and model:
  https://spar3d.github.io
paper_ai_summary: SPAR3D combines efficient point cloud generation with detailed mesh creation for high-fidelity single-image 3D reconstruction.
paper_ai_keywords: 3D object reconstruction regression-based modeling generative modeling visible surfaces occluded regions point diffusion model sparse 3D point clouds intermediate representation