You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Our work is closely related to a line of recent research on learning single-view 3D inference from registered 2D observations. Garg et al. [14] propose to learn a single-view depth estimation CNN using projection errors to a calibrated stereo twin for supervision. Concurrently, Deep3D [51] predicts a second stereo viewpoint from an input image using stereoscopic film footage as training data. A similar approach was taken by Godard et al. [16], with the addition of a left-right consistency constraint, and a better architecture design that led to impressive performance. Like our approach, these techniques only learn from image observations of the world, unlike methods that require explicit depth for training, e.g., [20, 42, 7, 27, 30].

Question: What is a calibrated stereo twin?
calibrated stereo twin is the supervision method used by  Garg et al. [14] to learn a single-view depth estimation CNN using projection errors.