Jon Barron
Minho Heo
Richard Strong Bowen
Learning to Autofocus
Left: Our Dataset. We provide a realistic dataset of 510 focal stacks captured "in the wild" along with a computed depth from SFM on 5 different views. These focal stacks have a large variation in color, texture, scene elements, and depth. Middle: Our Problem Formulation. We define Autofocus as three different problems: single-slice where the algorithm receives a single capture at a random starting point and then estimates the most in-focus index; focal stack where the algorithm receives the full focal stac
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, following "Learning single camera depth estimation using dual-pixels". Using this dataset, we apply modern deep classification models and an ordinal regression loss to obtain an efficient learning-b
Jon Barron
Minho Heo
Richard Strong Bowen