MicroDreamer: Efficient 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction

TL;DR

MicroDreamer introduces a fast, efficient 3D generation method using score-based iterative reconstruction, significantly reducing computation time while maintaining high quality across various 3D representations.

Contribution

It proposes SIR, a novel algorithm that enables pixel-space optimization for 3D generation, making the process 5-20 times faster than previous methods like SDS.

Findings

MicroDreamer generates 3D models in about 20 seconds on a single GPU.

It achieves comparable or better quality than existing methods like SDS and DreamGaussian.

The approach is versatile across different 3D representations and tasks.

Abstract

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 and the limitation of optimization confined to latent space. This paper introduces score-based iterative reconstruction (SIR), an efficient and general algorithm mimicking a differentiable 3D reconstruction process to reduce the NFEs and enable optimization in pixel space. 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, MicroDreamer is 5-20 times faster than SDS in generating neural radiance field while retaining a comparable performance and takes about 20 seconds to create meshes from 3D Gaussian splatting on a single A100 GPU, halving the time of the fastest optimization-based baseline DreamGaussian with significantly superior performance compared to the measurement standard deviation. Our code is available at https://github.com/ML-GSAI/MicroDreamer.