Learning Dense Correspondence via 3D Guided Cycle Consistency
Discriminative deep learning approaches have shown impressive results for problems where human labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4 cycle. We use ground truth synthetic to synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic to real, real to real and real to synthetic correspondences that are cycle consistent with the ground truth. At test time, no CAD models are required. We demonstrate that our end to end trained ConvNet supervised by cycle consistency outperforms state of the art pairwise matching methods in correspondence related tasks.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)