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: The key supervision signal for our depth and pose prediction CNNs comes from the task of novel view synthesis: given one input view of a scene, synthesize a new image of the scene seen from a different camera pose. We can synthesize a target view given a per-pixel depth in that image, plus the pose and visibility in a nearby view. As we will show next, this synthesis process can be implemented in a fully differentiable manner with CNNs as the geometry and pose estimation modules. Visibility can be handled, along with non-rigidity and other non-modeled factors, using an “explanability” mask, which we discuss later (Sec. 3.3). Another interesting area for future work would be to investigate in more detail the representation learned by our system. In particular, the pose network likely uses some form of image correspondence in estimating the camera motion, whereas the depth estimation network likely recognizes common structural features of scenes and objects. It would be interesting to probe these, and investigate the extent to which our network already performs, or could be re-purposed to perform, tasks such as object detection and semantic segmentation.

Question: How did the authors come to the conclusion that the pose network likely uses image correspondence and the depth estimation network likely recognizes common structural features?
New scenes can be synthesized given camera poses which will require image correspondence. Whereas  we can also synthesize a target view given a per-pixel depth in that image, plus the pose and visibility in a nearby view including structural features.