Learning Category-Specific 3D Shape Models From Weakly Labeled 2D Images

Recently, researchers have made great processes to build category-specific 3D shape models from 2D images with manual annotations consisting of class labels, keypoints, and ground truth figure-ground segmentations. However, the annotation of figure-ground segmentations is still labor-intensive and time-consuming. To further alleviate the burden of providing such manual annotations, we make the earliest effort to learn category-specific 3D shape models by only using weakly labeled 2D images. By revealing the underlying relationship between the tasks of common object segmentation and category-specific 3D shape reconstruction, we propose a novel framework to jointly solve these two problems along a cluster-level learning curriculum. Comprehensive experiments on the challenging PASCAL VOC benchmark demonstrate that the category-specific 3D shape models trained using our weakly supervised learning framework could, to some extent, approach the performance of the state-of-the-art methods using expensive manual segmentation annotations. In addition, the experiments also demonstrate the effectiveness of using 3D shape models for helping common object segmentation.