  3D reconstruction from a single view image is a long-standing prob-lem in
computer vision. Various methods based on different shape representations(such
as point cloud or volumetric representations) have been proposed. However,the
3D shape reconstruction with fine details and complex structures are still
chal-lenging and have not yet be solved. Thanks to the recent advance of the
deepshape representations, it becomes promising to learn the structure and
detail rep-resentation using deep neural networks. In this paper, we propose a
novel methodcalled STD-Net to reconstruct the 3D models utilizing the mesh
representationthat is well suitable for characterizing complex structure and
geometry details.To reconstruct complex 3D mesh models with fine details, our
method consists of(1) an auto-encoder network for recovering the structure of
an object with bound-ing box representation from a single image, (2) a
topology-adaptive graph CNNfor updating vertex position for meshes of complex
topology, and (3) an unifiedmesh deformation block that deforms the structural
boxes into structure-awaremeshed models. Experimental results on the images
from ShapeNet show that ourproposed STD-Net has better performance than other
state-of-the-art methods onreconstructing 3D objects with complex structures
and fine geometric details.
