  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}.
