  Estimating depth from a single RGB image is an ill-posed and inherently
ambiguous problem. State-of-the-art deep learning methods can now estimate
accurate 2D depth maps, but when the maps are projected into 3D, they lack
local detail and are often highly distorted. We propose a fast-to-train
two-streamed CNN that predicts depth and depth gradients, which are then fused
together into an accurate and detailed depth map. We also define a novel set
loss over multiple images; by regularizing the estimation between a common set
of images, the network is less prone to over-fitting and achieves better
accuracy than competing methods. Experiments on the NYU Depth v2 dataset shows
that our depth predictions are competitive with state-of-the-art and lead to
faithful 3D projections.
