Monocular 3D reconstruction is to reconstruct the shape of object and its
other information from a single RGB image. In 3D reconstruction, polygon mesh,
with detailed surface information and low computational cost, is the most
prevalent expression form obtained from deep learning models. However, the
state-of-the-art schemes fail to directly generate well-structured meshes, and
we identify that most meshes have severe Vertices Clustering (VC) and Illegal
Twist (IT) problems. By analyzing the mesh deformation process, we pinpoint
that the inappropriate usage of Chamfer Distance (CD) loss is a root cause of
VC and IT problems in deep learning model. In this paper, we initially
demonstrate these two problems induced by CD loss with visual examples and
quantitative analyses. Then, we propose a fine-grained reconstruction method
CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive
deformation. Extensive experiments on two 3D datasets and comparisons with five
latest schemes demonstrate that our CD$^2$ directly generates a well-structured
mesh and outperforms others in terms of several quantitative metrics.

本文提出通过两次使用 Chamfer 距离来进行适当的变形，从而直接生成结构良好的网格的方法 CD$^2$，并在两个 3D 数据集上进行实验证明其在多个定量指标上优于其他方法。