MicroDreamer: Zero-shot 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
<|reference_start|>MicroDreamer: Zero-shot 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction: 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 mimicking a differentiable 3D reconstruction process to reduce the NFEs. Given a single set of images sampled from a multi-view score-based diffusion model, SIR repeatedly optimizes 3D parameters, unlike the single-step optimization in SDS. With other improvements in training, 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 splatting on a single A100 GPU, halving the time of the fastest zero-shot baseline, DreamGaussian. Our code is available at \url{https://github.com/ML-GSAI/MicroDreamer}.<|reference_end|>
@article{chen2024microdreamer:,
    title={MicroDreamer: Efficient 3D Generation in $\sim$20 Seconds by Score-based
  Iterative Reconstruction},
    author={Luxi Chen, Zhengyi Wang, Zihan Zhou, Tingting Gao, Hang Su, Jun Zhu,
  Chongxuan Li},
    journal={arXiv preprint arXiv:2404.19525},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19525},
    primaryClass={cs.CV}
}