MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model

Minghua Liu*1,2†, Chong Zeng*3‡, Xinyue Wei1,2†, Ruoxi Shi1,2†, Linghao Chen2,3†, Chao Xu2,4†, Mengqi Zhang2, Zhaoning Wang2, Xiaoshuai Zhang1,2†, Isabella Liu1, Hongzhi Wu3, Hao Su1,2
1UC San Diego, 2Hillbot Inc., 3Zhejiang University, 4UCLA
* Equal contribution.
† Work done during internship at Hillbot Inc.
‡ Work done during internship at UC San Diego.
arXiv Demo Results (GSO & OmniObject3D)

MeshFormer reconstructs high-quality 3D textured meshes with fine-grained, sharp geometric details in a single feed-forward pass that takes just a few seconds. MeshFormer can be trained using 8 H100 GPUs for just 2 days, whereas concurrent works require more than one hundred.


Abstract

Open-world 3D reconstruction models have recently garnered significant attention. However, without sufficient 3D inductive bias, existing methods typically entail expensive training costs and struggle to extract high-quality 3D meshes. In this work, we introduce MeshFormer, a sparse-view reconstruction model that explicitly leverages 3D native structure, input guidance, and training supervision. Specifically, instead of using a triplane representation, we store features in 3D sparse voxels and combine transformers with 3D convolutions to leverage an explicit 3D structure and the projective bias. In addition to sparse-view RGB input, we require the network to take as input and generate corresponding normal maps. The input normal maps can be predicted by 2D diffusion models, significantly aiding in the guidance and refinement of the geometry's learning. Moreover, by combining Signed Distance Function (SDF) supervision with surface rendering, we directly learn to generate high-quality meshes without the need for complex multi-stage training processes. By incorporating these explicit 3D biases, MeshFormer can be trained efficiently and deliver high-quality textured meshes with fine-grained geometric details. It can also be integrated with 2D diffusion models to enable fast single-image-to-3D and text-to-3D tasks.


Method Overview

MeshFormer takes a sparse set of multi-view RGB and normal images as input, which can be estimated using existing 2D diffusion models. We utilize a 3D feature volume representation, and submodules Voxel Former and Sparse Voxel Former share a similar novel architecture, detailed in the gray region. We train MeshFormer in a unified single stage by combining mesh surface rendering and 512^3 SDF supervision. MeshFormer learns an additional normal texture, which can be used to further enhance the geometry and generate fine-grained sharp geometric details.



Single Image to 3D

Qualitative Examples from the GSO dataset. Both the textured and textureless mesh renderings are shown. Please zoom in to examine details and mesh quality.



Application: Text to 3D



Geometry Enhancement

We task MeshFormer with outputting an additional 3D normal map, which can be used for geometry enhancement and generating sharper geometric details. This is achieved by applying a traditional algorithm as a post-processing step that aligns the mesh vertices with the normals.

BibTeX

@article{liu2024meshformer,
  title={MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model},
  author={Minghua Liu and Chong Zeng and Xinyue Wei and Ruoxi Shi and Linghao Chen and Chao Xu and Mengqi Zhang and Zhaoning Wang and Xiaoshuai Zhang and Isabella Liu and Hongzhi Wu and Hao Su},
  journal={arXiv preprint arXiv:2408.10198},
  year={2024}
}