  3D point cloud generation by the deep neural network from a single image has
been attracting more and more researchers' attention. However,
recently-proposed methods require the objects be captured with relatively clean
backgrounds, fixed viewpoint, while this highly limits its application in the
real environment. To overcome these drawbacks, we proposed to integrate the
prior 3D shape knowledge into the network to guide the 3D generation. By taking
additional 3D information, the proposed network can handle the 3D object
generation from a single real image captured from any viewpoint and complex
background. Specifically, giving a query image, we retrieve the nearest shape
model from a pre-prepared 3D model database. Then, the image together with the
retrieved shape model is fed into the proposed network to generate the
fine-grained 3D point cloud. The effectiveness of our proposed framework has
been verified on different kinds of datasets. Experimental results show that
the proposed framework achieves state-of-the-art accuracy compared to other
volumetric-based and point set generation methods. Furthermore, the proposed
framework works well for real images in complex backgrounds with various view
angles.
