MeshLRM: Large Reconstruction Model for High-Quality Mesh
<|reference_start|>MeshLRM: Large Reconstruction Model for High-Quality Mesh: 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/<|reference_end|>
@article{wei2024meshlrm:,
    title={MeshLRM: Large Reconstruction Model for High-Quality Mesh},
    author={Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin
  Deschaintre, Kalyan Sunkavalli, Hao Su, Zexiang Xu},
    journal={arXiv preprint arXiv:2404.12385},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12385},
    primaryClass={cs.CV cs.GR}
}