title: MicroDreamer: Zero-shot 3D Generation in sim20 Seconds by Score-based
    Iterative Reconstruction
summary: Optimization-based approaches, such as score distillation sampling (SDS),
  show promise in zero-shot 3D generation but suffer from low efficiency,
  primarily due to the high number of function evaluations (NFEs) required for
  each sample. In this paper, we introduce score-based iterative reconstruction
  (SIR), an efficient and general algorithm for 3D generation with a multi-view
  score-based diffusion model. Given the images produced by the diffusion model,
  SIR reduces NFEs by repeatedly optimizing 3D parameters, unlike the single
  optimization in SDS, mimicking the 3D reconstruction process. With other
  improvements including optimization in the pixel space, we present an efficient
  approach called MicroDreamer that generally applies to various 3D
  representations and 3D generation tasks. In particular, retaining a comparable
  performance, MicroDreamer is 5-20 times faster than SDS in generating neural
  radiance field and takes about 20 seconds to generate meshes from 3D Gaussian
  splitting on a single A100 GPU, halving the time of the fastest zero-shot
  baseline, DreamGaussian. Our code is available at
  https://github.com/ML-GSAI/MicroDreamer.
paper_authors._id: 6631c599cc6a204ac6351734
paper_authors.name: Luxi Chen
paper_authors._id: 6631c599cc6a204ac6351735
paper_authors.name: Zhengyi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:29:44.275Z
paper_authors._id: 634e15aec1ce28f1de91c470
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634e15aec1ce28f1de91c470/MdtJKgE_fbHydhqESr-DH.png
paper_authors.fullname: Zhengyi Wang
paper_authors.type: user
paper_authors.user: Zhengyi
paper_authors._id: 6631c599cc6a204ac6351736
paper_authors.name: Chongxuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:29:49.940Z
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: 6631c599cc6a204ac6351737
paper_authors.name: Tingting Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:29:56.380Z
paper_authors._id: 656453832bdaccfcd5379431
paper_authors.avatarUrl: /avatars/a0d764ce6b3fd05532c7a9cb2f263e33.svg
paper_authors.fullname: Gao Ting
paper_authors.type: user
paper_authors.user: TingTingGao
paper_authors._id: 6631c599cc6a204ac6351738
paper_authors.name: Hang Su
paper_authors._id: 6631c599cc6a204ac6351739
paper_authors.name: Jun Zhu
paper_title: MicroDreamer: Zero-shot 3D Generation in sim20 Seconds by Score-based
    Iterative Reconstruction
paper_summary: Optimization-based approaches, such as score distillation sampling (SDS),
  show promise in zero-shot 3D generation but suffer from low efficiency,
  primarily due to the high number of function evaluations (NFEs) required for
  each sample. In this paper, we introduce score-based iterative reconstruction
  (SIR), an efficient and general algorithm for 3D generation with a multi-view
  score-based diffusion model. Given the images produced by the diffusion model,
  SIR reduces NFEs by repeatedly optimizing 3D parameters, unlike the single
  optimization in SDS, mimicking the 3D reconstruction process. With other
  improvements including optimization in the pixel space, we present an efficient
  approach called MicroDreamer that generally applies to various 3D
  representations and 3D generation tasks. In particular, retaining a comparable
  performance, MicroDreamer is 5-20 times faster than SDS in generating neural
  radiance field and takes about 20 seconds to generate meshes from 3D Gaussian
  splitting on a single A100 GPU, halving the time of the fastest zero-shot
  baseline, DreamGaussian. Our code is available at
  https://github.com/ML-GSAI/MicroDreamer.
paper_ai_summary: Score-based iterative reconstruction (SIR) enhances zero-shot 3D generation efficiency compared to score distillation sampling by optimizing 3D parameters iteratively, reducing the number of function evaluations and achieving faster generation times.
paper_ai_keywords: score distillation sampling score-based iterative reconstruction multi-view score-based diffusion model neural radiance field 3D Gaussian splitting MicroDreamer DreamGaussian