How to reconstruct the complete 3D mesh model from a single natural image is now still considered as a challenging problem. Most existing methods describe 3D shapes in the form of voxel or point cloud, and it is not always trivial to convert them into meshes with high quality. In this paper, we present a novel method to effectively address this problem by using a specially-designed GAN model to map a given natural image to a geometry image, from which the corresponding 3D mesh can be reconstructed. Specifically, we disentangle the tasks of viewpoint estimation and 3D reconstruction, ensuring that the reconstruction network focuses on generating vivid 3D meshes with accurate viewpoint information. We also add a differentiable module to create silhouettes from various viewpoints for the synthesized geometry image, aiming to improve the consistency between the generated 3D model and its input 2D image. Furthermore, we design a compact but effective discriminator for geometry images to guarantee a plausible overall contour of the generated object. Experiments conducted on a publicly available database demonstrate that the proposed method can generate 3D meshes with high fidelity and outperforms other state-of-the-art approaches in both qualitative and quantitative results. Our code is publicly available at https://github.com/tasx0823/EasyMesh.

现在仍然如何从单个自然图像重建完整的3D网格模型仍然是一个具有挑战性的问题。现有的大多数方法都以体素或点云的形式描述3D形状，将它们转换为高质量的网格并不总是一件容易的事。在本文中，我们提出了一种新颖的方法来有效解决此问题，方法是使用专门设计的GAN模型将给定的自然图像映射到几何图像，从中可以重构相应的3D网格。具体来说，我们将视点估计和3D重建的任务分开处理，确保重建网络专注于生成具有准确视点信息的生动3D网格。我们还添加了一个微分模块，以从各个角度为合成的几何图像创建轮廓，旨在提高生成的3D模型与其输入2D图像之间的一致性。此外，我们为几何图像设计了紧凑但有效的鉴别器，以确保所生成对象的整体轮廓合理。在可公开获取的数据库上进行的实验表明，该方法可以生成高保真度的3D网格，并且在定性和定量结果方面均优于其他最新方法。我们的代码可从https://github.com/tasx0823/EasyMesh公开获得。在可公开获得的数据库上进行的实验表明，该方法可以生成高保真度的3D网格，并且在定性和定量结果上均优于其他最新方法。我们的代码可从https://github.com/tasx0823/EasyMesh公开获得。在可公开获取的数据库上进行的实验表明，该方法可以生成高保真度的3D网格，并且在定性和定量结果方面均优于其他最新方法。我们的代码可从https://github.com/tasx0823/EasyMesh公开获得。