With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life. Yet creating 3D shapes is arduous and requires significant professional knowledge. Meanwhile, Deep learning has enabled high-quality 3D shape reconstruction from various sources, making it a viable approach to acquiring 3D shapes with minimal effort. Importantly, to be used in common 3D applications, the reconstructed shapes need to be represented as polygonal meshes, which is a challenge for neural networks due to the irregularity of mesh tessellations. In this survey, we provide a comprehensive review of mesh reconstruction methods that are powered by machine learning. We first describe various representations for 3D shapes in the deep learning context. Then we review the development of 3D mesh reconstruction methods from voxels, point clouds, single images, and multi-view images. Finally, we identify several challenges in this field and propose potential future directions.

翻译：随着硬件与渲染技术的近期进展，三维模型已广泛渗透至日常生活。然而，三维形状的创建仍是一项艰巨任务，需要深厚的专业知识。与此同时，深度学习实现了从多种数据源对三维形状的高质量重建，为以极小成本获取三维形状提供了可行途径。值得注意的是，为适配主流三维应用场景，重建形状需表示为多边形网格，而网格细分的非规则性为神经网络带来了挑战。本综述全面回顾了由机器学习驱动的网格重建方法。我们首先阐述深度学习背景下三维形状的多种表征方式，继而梳理从体素、点云、单幅图像及多视角图像进行三维网格重建方法的发展脉络。最后，我们指出该领域存在的若干挑战，并展望潜在未来方向。