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MeshLRM: Large Reconstruction Model for High-Quality Meshes

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arxiv 2404.12385 v2 pith:5OTBQLLG submitted 2024-04-18 cs.CV cs.GR

Xinyue Wei , Kai Zhang , Sai Bi , Hao Tan , Fujun Luan , Valentin Deschaintre , Kalyan Sunkavalli , Hao Su
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Zexiang Xu
This is my paper · ORCID
classification cs.CVcs.GR
keywords meshmeshlrmreconstructionallowsapproachhigh-qualityimageslarge
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose MeshLRM, a novel LRM-based approach that can reconstruct a high-quality mesh from merely four input images in less than one second. Different from previous large reconstruction models (LRMs) that focus on NeRF-based reconstruction, MeshLRM incorporates differentiable mesh extraction and rendering within the LRM framework. This allows for end-to-end mesh reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering. Moreover, we improve the LRM architecture by simplifying several complex designs in previous LRMs. MeshLRM's NeRF initialization is sequentially trained with low- and high-resolution images; this new LRM training strategy enables significantly faster convergence and thereby leads to better quality with less compute. Our approach achieves state-of-the-art mesh reconstruction from sparse-view inputs and also allows for many downstream applications, including text-to-3D and single-image-to-3D generation. Project page: https://sarahweiii.github.io/meshlrm/

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    A feed-forward VAE plus rectified-flow model animates arbitrary static meshes from text prompts in seconds, with a new 4M-sequence training dataset.

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