  We propose a learning-based depth from focus/defocus (DFF), which takes a
focal stack as input for estimating scene depth. Defocus blur is a useful cue
for depth estimation. However, the size of the blur depends on not only scene
depth but also camera settings such as focus distance, focal length, and
f-number. Current learning-based methods without any defocus models cannot
estimate a correct depth map if camera settings are different at training and
test times. Our method takes a plane sweep volume as input for the constraint
between scene depth, defocus images, and camera settings, and this intermediate
representation enables depth estimation with different camera settings at
training and test times. This camera-setting invariance can enhance the
applicability of learning-based DFF methods. The experimental results also
indicate that our method is robust against a synthetic-to-real domain gap, and
exhibits state-of-the-art performance.
