  3D object reconstruction from a single-view image is a long-standing
challenging problem. Previous work was difficult to accurately reconstruct 3D
shapes with a complex topology which has rich details at the edges and corners.
Moreover, previous works used synthetic data to train their network, but domain
adaptation problems occurred when tested on real data. In this paper, we
propose a Dynamic Multi-branch Information Fusion Network (DmifNet) which can
recover a high-fidelity 3D shape of arbitrary topology from a 2D image.
Specifically, we design several side branches from the intermediate layers to
make the network produce more diverse representations to improve the
generalization ability of network. In addition, we utilize DoG (Difference of
Gaussians) to extract edge geometry and corners information from input images.
Then, we use a separate side branch network to process the extracted data to
better capture edge geometry and corners feature information. Finally, we
dynamically fuse the information of all branches to gain final predicted
probability. Extensive qualitative and quantitative experiments on a
large-scale publicly available dataset demonstrate the validity and efficiency
of our method. Code and models are publicly available at
https://github.com/leilimaster/DmifNet.
