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：随着硬件和成型技术的最近进步,我们生活中到处出现了三维模型。然而,创造三维模型是艰巨的,需要大量专业知识。与此同时,深层学习使各种来源的三维模型能够进行高质量的三维模型重建,使三维模型的重建成为一个可行的方法,以尽量少的努力获得三维形状。重要的是,在通用的三维应用中,重建后的形状需要以多边形藻来代表,这是神经网络面临的一个挑战,因为网状熔融不规则。在这次调查中,我们全面审查了机器学习所驱动的网状重建方法。我们首先描述了在深层学习背景下三维形状的各种表现。然后我们从 voxel、点云、单一图像和多视图图像中审视了三维网形重建方法的开发情况。最后,我们找出了该领域的若干挑战,并提出未来方向。</s>