Vanet: a View Attention Guided Network for 3d Reconstruction from Single and Multi-View Images

Yi Yuan, Jilin Tang, Zhengxia Zou

Abstract: Reconstructing 3D meshes of objects from 2D images is an important but challenging task. Previous 3D reconstruction methods either only focus on generating the mesh from a single image, or multi-view images. Instead of investigating these problems separately, we present a novel view attention guided network called VANet which addresses both single and multi-view 3D reconstruction under a unified frame-work. To explore non-visible parts of an object during the re-construction, a channel-wise view attention mechanism and a dual pathway network architecture are introduced. The proposed network highlights the informative object parts and compensates those non-informative ones with auxiliary views of input.