Mesh models are a promising approach for encoding the structure of 3D
objects. Current mesh reconstruction systems predict uniformly distributed
vertex locations of a predetermined graph through a series of graph
convolutions, leading to compromises with respect to performance or resolution.
In this paper, we argue that the graph representation of geometric objects
allows for additional structure, which should be leveraged for enhanced
reconstruction. Thus, we propose a system which properly benefits from the
advantages of the geometric structure of graph encoded objects by introducing
(1) a graph convolutional update preserving vertex information; (2) an adaptive
splitting heuristic allowing detail to emerge; and (3) a training objective
operating both on the local surfaces defined by vertices as well as the global
structure defined by the mesh. Our proposed method is evaluated on the task of
3D object reconstruction from images with the ShapeNet dataset, where we
demonstrate state of the art performance, both visually and numerically, while
having far smaller space requirements by generating adaptive meshes

本文提出了一种基于图卷积的自适应分裂启发式方法，利用几何对象的图表示的附加结构来提高重建，优化本地表面和全局结构，应用于 ShapeNet 数据集的 3D 物体重建任务中，生成了具有最先进表现的自适应网格。