Abstract
3D reconstruction from single image is a classical problem in computer vision. Due to the fact that the information contained in one single image is not sufficient for 3D shape reconstruction, the existing model cannot reconstruct 3D models very well. To tackle this problem, we propose a novel model which effectively utilizes the category information of objects to improve the performance of network on single view 3D reconstruction. Our model consists of two parts: rough shape generation network (RSGN) and category comparison network (CCN). RSGN can learn the characteristics of objects in the same category through the comparison part CCN. In the experiments, we verify the feasibility of our model on the ShapeNet dataset, and the results confirm our framework.
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