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/
6621f53711047ec8884147b2
Xinyue Wei
admin_assigned
2024-04-19T08:28:25.090Z
646d6615da8e99940b7143cf
/avatars/e6e24e993fc4f3e9c7026b4c34bd1a25.svg
Xinyue Wei
user
sarahwei0210
6621f53711047ec8884147b3
Kai Zhang
6621f53711047ec8884147b4
Sai Bi
6621f53711047ec8884147b5
Hao Tan
6621f53711047ec8884147b6
Fujun Luan
6621f53711047ec8884147b7
Valentin Deschaintre
admin_assigned
2024-04-19T08:30:04.563Z
63e0dc30ab3fb81c39816ce7
/avatars/a30c8ca8850ae120dd409b6f7d204493.svg
Valentin Deschaintre
user
deschain
6621f53711047ec8884147b8
Kalyan Sunkavalli
6621f53711047ec8884147b9
Hao Su
6621f53711047ec8884147ba
Zexiang Xu
admin_assigned
2024-04-19T08:30:15.052Z
65565b1b8616d8d740abb976
/avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
Zexiang Xu
user
zexiangxu
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/
MeshLRM, an LRM-based approach, reconstructs high-quality meshes efficiently from four images using differentiable mesh extraction and rendering, enhancing NeRF-based methods.
LRM MeshLRM differentiable mesh extraction rendering NeRF end-to-end mesh reconstruction NeRF LRM sequential training low-resolution images high-resolution images faster convergence state-of-the-art mesh reconstruction text-to-3D single-image-to-3D generation