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It has long been an ill-posed problem to predict absolute depth maps fromsingle images in real (unseen) indoor scenes. We observe that it is essentiallydue to not only the scale-ambiguous problem but also the focal-ambiguousproblem that decreases the generalization ability of monocular depthestimation. That is, images may be captured by cameras of different focallengths in scenes of different scales. In this paper, we develop afocal-and-scale depth estimation model to well learn absolute depth maps fromsingle images in unseen indoor scenes. First, a relative depth estimationnetwork is adopted to learn relative depths from single images with diversescales/semantics. Second, multi-scale features are generated by mapping asingle focal length value to focal length features and concatenating them withintermediate features of different scales in relative depth estimation.Finally, relative depths and multi-scale features are jointly fed into anabsolute depth estimation network. In addition, a new pipeline is developed toaugment the diversity of focal lengths of public datasets, which are oftencaptured with cameras of the same or similar focal lengths. Our model istrained on augmented NYUDv2 and tested on three unseen datasets. Our modelconsiderably improves the generalization ability of depth estimation by 41%/13%(RMSE) with/without data augmentation compared with five recent SOTAs and wellalleviates the deformation problem in 3D reconstruction. Notably, our modelwell maintains the accuracy of depth estimation on original NYUDv2.

FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene