FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen
  Indoor Scene
Chengrui Wei Meng Yang Lei He Nanning Zheng
It has long been an ill-posed problem to predict absolute depth maps from
single images in real (unseen) indoor scenes. We observe that it is essentially
due to not only the scale-ambiguous problem but also the focal-ambiguous
problem that decreases the generalization ability of monocular depth
estimation. That is, images may be captured by cameras of different focal
lengths in scenes of different scales. In this paper, we develop a
focal-and-scale depth estimation model to well learn absolute depth maps from
single images in unseen indoor scenes. First, a relative depth estimation
network is adopted to learn relative depths from single images with diverse
scales/semantics. Second, multi-scale features are generated by mapping a
single focal length value to focal length features and concatenating them with
intermediate features of different scales in relative depth estimation.
Finally, relative depths and multi-scale features are jointly fed into an
absolute depth estimation network. In addition, a new pipeline is developed to
augment the diversity of focal lengths of public datasets, which are often
captured with cameras of the same or similar focal lengths. Our model is
trained on augmented NYUDv2 and tested on three unseen datasets. Our model
considerably improves the generalization ability of depth estimation by 41%/13%
(RMSE) with/without data augmentation compared with five recent SOTAs and well
alleviates the deformation problem in 3D reconstruction. Notably, our model
well maintains the accuracy of depth estimation on original NYUDv2.