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GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting

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arxiv 2404.19702 v1 pith:2QJGFCAX submitted 2024-04-30 cs.CV

GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting

Kai Zhang , Sai Bi , Hao Tan , Yuanbo Xiangli , Nanxuan Zhao , Kalyan Sunkavalli , Zexiang Xu This is my paper
classification cs.CV
keywords modelgs-lrmgaussianlargeimagesper-pixelposedproject
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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/ .

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