论文标题
学习自动对焦
Learning to Autofocus
论文作者
论文摘要
自动对焦是数码相机的重要任务，但是当前的方法通常表现出较差的性能。我们建议针对此问题的基于学习的方法，并提供一个足够大小的现实数据集，以进行有效学习。我们的数据集在“使用双像素学习单相机深度估算”之后，标有从多视图立体声获得的每个像素深度。使用此数据集，我们应用现代的深层分类模型和序数回归损失来获得有效的基于学习的自动对焦技术。我们证明，与以前的学习方法和非学习方法相比，我们的方法提供了显着的改进：我们的模型将平均绝对误差降低了最佳相当基线算法的3.6倍。我们的数据集和代码公开可用。
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-based autofocus technique. We demonstrate that our approach provides a significant improvement compared with previous learned and non-learned methods: our model reduces the mean absolute error by a factor of 3.6 over the best comparable baseline algorithm. Our dataset and code are publicly available.
