  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.
