  We propose a self-supervised training approach for learning view-invariant
dense visual descriptors using image augmentations. Unlike existing works,
which often require complex datasets, such as registered RGBD sequences, we
train on an unordered set of RGB images. This allows for learning from a single
camera view, e.g., in an existing robotic cell with a fix-mounted camera. We
create synthetic views and dense pixel correspondences using data
augmentations. We find our descriptors are competitive to the existing methods,
despite the simpler data recording and setup requirements. We show that
training on synthetic correspondences provides descriptor consistency across a
broad range of camera views. We compare against training with geometric
correspondence from multiple views and provide ablation studies. We also show a
robotic bin-picking experiment using descriptors learned from a fix-mounted
camera for defining grasp preferences.
