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/.

翻译：我们提出GRM，一种能够在约0.1秒内从稀疏视角图像恢复三维资产的大规模重建器。GRM是一个基于Transformer的前馈模型，能够高效整合多视角信息，将输入像素转换为像素对齐的高斯体，并通过反投影生成一组密集分布的三维高斯体以表示场景。我们的Transformer架构与三维高斯体的结合，共同构建了一个可扩展且高效的重建框架。大量实验结果表明，本方法在重建质量和效率方面均优于现有替代方案。我们还展示了GRM在生成任务（即文本到三维和图像到三维）中的应用潜力，通过将其与现有多视角扩散模型集成实现。项目网站：https://justimyhxu.github.io/projects/grm/。