Hu, FeiYang, XinyanZhong, WeiYe, LongZhang, QinFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes2018-10-072018-10-072018978-3-03868-073-4https://doi.org/10.2312/pg.20181279https://diglib.eg.org:443/handle/10.2312/pg201812793D object reconstruction from single view image is a challenge task. Due to the fact that the information contained in one isolated image is not sufficient for reasonable 3D shape reconstruction, the existing results on single-view 3D reconstruction always lack marginal voxels. To tackle this problem, we propose a parallel system named 3D VAE-attention network (3VAN) for single view 3D reconstruction. Distinct from the common encoder-decoder structure, the proposed network consists of two parallel branches, 3D-VAE and Attention Network. 3D-VAE completes the general shape reconstruction by an extension of standard VAE model, and Attention Network supplements the missing details by a 3D reconstruction attention network. In the experiments, we verify the feasibility of our 3VAN on the ShapeNet and PASCAL 3D+ datasets. By comparing with the state-of-art methods, the proposed 3VAN can produce more precise 3D object models in terms of both qualitative and quantitative evaluation.Computing methodologiesReconstructionVolumetric models10.2312/pg.2018127953-56