Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance
This work solves the issue of camera-setting dependency in learning-based depth from focus/defocus methods, enhancing applicability for computer vision tasks, though it is incremental.
The paper tackles the problem of depth estimation from focal stacks by addressing camera-setting invariance, achieving state-of-the-art performance with robustness to synthetic-to-real domain gaps.
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.