Single-view 3D Reconstruction Algorithm Based on View-aware

While projecting 3D shapes to 2D images is irreversible due to the abandoned dimension amid the projection process, there are rapidly growing interests across various vertical industries for 3D reconstruction techniques, from visualization purposes to computer aided geometric design. The traditional 3D reconstruction approaches based on depth map or RGB image can synthesize visually satisfactory 3D objects, while they generally suffer from several problems: (1)The 2D to 3D learning strategy is brutal-force; (2)Unable to solve the effects of differences in appearance from different viewpoints of objects; (3)Multiple images from distinctly different viewpoints are required. In this paper, an end-to-end View-Aware 3D (VA3D) reconstruction network is proposed to address the above problems. In particular, the VA3D includes a multi-neighbor-view synthesis sub-network and a 3D reconstruction sub-network. The multi-neighbor-view synthesis sub-network generates multiple neighboring viewpoint images based on the object source view, while the adaptive fusional module is added to resolve the blurry and distortion issues in viewpoint translation. The 3D reconstruction sub-network introduces a recurrent neural network to recover the object 3D shape from multi-view sequence. Extensive qualitative and quantitative experiments on the ShapeNet dataset show that the VA3D effectively improves the 3D reconstruction results based on single-view.