  We propose GS-LRM, a scalable large reconstruction model that can predict
high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23
seconds on single A100 GPU. Our model features a very simple transformer-based
architecture; we patchify input posed images, pass the concatenated multi-view
image tokens through a sequence of transformer blocks, and decode final
per-pixel Gaussian parameters directly from these tokens for differentiable
rendering. In contrast to previous LRMs that can only reconstruct objects, by
predicting per-pixel Gaussians, GS-LRM naturally handles scenes with large
variations in scale and complexity. We show that our model can work on both
object and scene captures by training it on Objaverse and RealEstate10K
respectively. In both scenarios, the models outperform state-of-the-art
baselines by a wide margin. We also demonstrate applications of our model in
downstream 3D generation tasks. Our project webpage is available at:
https://sai-bi.github.io/project/gs-lrm/ .
