MeshLRM: Large Reconstruction Model for High-Quality Mesh
Authors: name: Xinyue Wei; name: Kai Zhang; name: Sai Bi; name: Hao Tan; name: Fujun Luan; name: Valentin Deschaintre; name: Kalyan Sunkavalli; name: Hao Su; name: Zexiang Xu
Published: 2024-04-18 17:59:41

Abstract: 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/