GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction
  and Generation
Yinghao Xu Zifan Shi Wang Yifan Hansheng Chen Ceyuan Yang Sida Peng Yujun Shen Gordon Wetzstein
We introduce GRM, a large-scale reconstructor capable of recovering a 3D
asset from sparse-view images in around 0.1s. GRM is a feed-forward
transformer-based model that efficiently incorporates multi-view information to
translate the input pixels into pixel-aligned Gaussians, which are unprojected
to create a set of densely distributed 3D Gaussians representing a scene.
Together, our transformer architecture and the use of 3D Gaussians unlock a
scalable and efficient reconstruction framework. Extensive experimental results
demonstrate the superiority of our method over alternatives regarding both
reconstruction quality and efficiency. We also showcase the potential of GRM in
generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with
existing multi-view diffusion models. Our project website is at:
https://justimyhxu.github.io/projects/grm/.