Reconstructing the 3D mesh of a general object from a single image is now
possible thanks to the latest advances of deep learning technologies. However,
due to the nontrivial difficulty of generating a feasible mesh structure, the
state-of-the-art approaches often simplify the problem by learning the
displacements of a template mesh that deforms it to the target surface. Though
reconstructing a 3D shape with complex topology can be achieved by deforming
multiple mesh patches, it remains difficult to stitch the results to ensure a
high meshing quality. In this paper, we present an end-to-end single-view mesh
reconstruction framework that is able to generate high-quality meshes with
complex topologies from a single genus-0 template mesh. The key to our approach
is a novel progressive shaping framework that alternates between mesh
deformation and topology modification. While a deformation network predicts the
per-vertex translations that reduce the gap between the reconstructed mesh and
the ground truth, a novel topology modification network is employed to prune
the error-prone faces, enabling the evolution of topology. By iterating over
the two procedures, one can progressively modify the mesh topology while
achieving higher reconstruction accuracy. Moreover, a boundary refinement
network is designed to refine the boundary conditions to further improve the
visual quality of the reconstructed mesh. Extensive experiments demonstrate
that our approach outperforms the current state-of-the-art methods both
qualitatively and quantitatively, especially for the shapes with complex
topologies.

该论文提出了一种基于深度学习的单视图网格重建框架，能够从单个 0 维模板网格生成具有复杂拓扑结构的高质量网格，其关键在于交替使用网格变形和拓扑修改两个网络。