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/.

我们介绍了GRM，一个能够从稀疏视图图像中在大约0.1秒内恢复3D资产的大规模重构器。GRM是一个前馈变换器模型，能够高效地将多视图信息结合起来，将输入像素翻译成像素对齐的高斯分布，通过反投影生成一系列密集分布的3D高斯分布来表示场景。我们的变换器架构和使用3D高斯分布的方法共同构成了一个可扩展且高效的重构框架。广泛的实验结果证明，我们的方法在重构质量和效率方面优于其他替代方法。我们还展示了GRM在生成任务中的潜力，即将其与现有的多视图扩散模型集成，如文本到3D和图像到3D。有关我们的项目网站，请访问：this https URL

GRM：高效的大型高斯重建模型用于3D重建和生成