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3D point cloud generation by the deep neural network from a single image hasbeen attracting more and more researchers' attention. However,recently-proposed methods require the objects be captured with relatively cleanbackgrounds, fixed viewpoint, while this highly limits its application in thereal environment. To overcome these drawbacks, we proposed to integrate theprior 3D shape knowledge into the network to guide the 3D generation. By takingadditional 3D information, the proposed network can handle the 3D objectgeneration from a single real image captured from any viewpoint and complexbackground. Specifically, giving a query image, we retrieve the nearest shapemodel from a pre-prepared 3D model database. Then, the image together with theretrieved shape model is fed into the proposed network to generate thefine-grained 3D point cloud. The effectiveness of our proposed framework hasbeen verified on different kinds of datasets. Experimental results show thatthe proposed framework achieves state-of-the-art accuracy compared to othervolumetric-based and point set generation methods. Furthermore, the proposedframework works well for real images in complex backgrounds with various viewangles.

RealPoint3D: Point Cloud Generation from a Single Image with Complex Background