PGNet: A Part-based Generative Network for 3D object reconstruction

Deep-learning generative methods have developed rapidly. For example, various single- and multiview generative methods for meshes, voxels, and point clouds have been introduced. However, most 3D single-view reconstruction methods generate whole objects at one time, or in a cascaded way for dense structures, which misses local details of fine-grained structures. These methods are useless when the generative models are required to provide semantic information for parts. This paper proposes an efficient part-based recurrent generative network, which aims to generate object parts sequentially with the input of a single-view image and its semantic projection. The advantage of our method is its awareness of part structures; hence it generates more accurate models with fine-grained structures. Experiments show that our method attains high accuracy compared with other point set generation methods, particularly toward local details. (C) 2020 Published by Elsevier B.V.