Weakly-Supervised Reconstruction of 3D Objects with Large Shape Variation from Single In-the-Wild Images

Existing unsupervised 3D object reconstruction methods can not work well if the shape of objects varies substantially across images or if the images have distracting background. This paper proposes a novel learning framework for reconstructing 3D objects with large shape variation from single in-the-wild images. Considering that shape variation leads to appearance change of objects at various scales, we propose a fusion module to form combined multi-scale image features for 3D reconstruction. To deal with the ambiguity caused by shape variation, we propose side-output mask constraint to supervise the feature extraction, and adaptive edge constraint and initial shape constraint to supervise the shape reconstruction. Moreover, we propose background manipulation to augment the training images such that the obtained model is robust to background distraction. Extensive experiments have been done for both non-rigid objects (birds) and rigid objects (planes and vehicles), and the results prove the superiority of the proposed method.