SymGAN: Orientation Estimation without Annotation for Symmetric Objects

Training a computer vision system to predict an object's pose is crucial to improving robotic manipulation, where robots can easily locate and then grasp objects. Some of the key challenges in pose estimation lie in obtaining labeled data and handling objects with symmetries. We explore both these problems of viewpoint estimation (object 3D orientation) by proposing a novel unsupervised training paradigm that only requires a 3D model of the object of interest. We show that we can successfully train an orientation detector, which simply consumes an RGB image, in an adversarial training framework, where the discriminator learns to provide a learning signal to retrieve the object orientation using a black-box non differentiable renderer. In order to overcome this non differentiability, we introduce a randomized sampling method to obtain training gradients. To our knowledge this is the first time an adversarial framework is employed to successfully train a viewpoint detector that can handle symmetric objects.Using this training framework we show state of the art results on 3D orientation prediction on T-LESS, a challenging dataset for texture-less and symmetric objects.