Depth-from-defocus (DFD), modeling the relationship between depth and defocus
pattern in images, has demonstrated promising performance in depth estimation.
Recently, several self-supervised works try to overcome the difficulties in
acquiring accurate depth ground-truth. However, they depend on the all-in-focus
(AIF) images, which cannot be captured in real-world scenarios. Such limitation
discourages the applications of DFD methods. To tackle this issue, we propose a
completely self-supervised framework that estimates depth purely from a sparse
focal stack. We show that our framework circumvents the needs for the depth and
AIF image ground-truth, and receives superior predictions, thus closing the gap
between the theoretical success of DFD works and their applications in the real
world. In particular, we propose (i) a more realistic setting for DFD tasks,
where no depth or AIF image ground-truth is available; (ii) a novel
self-supervision framework that provides reliable predictions of depth and AIF
image under the challenging setting. The proposed framework uses a neural model
to predict the depth and AIF image, and utilizes an optical model to validate
and refine the prediction. We verify our framework on three benchmark datasets
with rendered focal stacks and real focal stacks. Qualitative and quantitative
evaluations show that our method provides a strong baseline for self-supervised
DFD tasks.

提出了一种基于稀疏焦距堆栈的自监督 DFD 框架，无需深度或 AIF 图像基础知识，利用神经模型预测深度和 AIF 图像，并利用光学模型验证和精化预测。

基于散焦线索的全自监督深度估计

Fully Self-Supervised Depth Estimation from Defocus Clue

Depth estimation is of critical interest for scene understanding and accurate
3D reconstruction. Most recent approaches in depth estimation with deep
learning exploit geometrical structures of standard sharp images to predict
corresponding depth maps. However, cameras can also produce images with defocus
blur depending on the depth of the objects and camera settings. Hence, these
features may represent an important hint for learning to predict depth. In this
paper, we propose a full system for single-image depth prediction in the wild
using depth-from-defocus and neural networks. We carry out thorough experiments
to test deep convolutional networks on real and simulated defocused images
using a realistic model of blur variation with respect to depth. We also
investigate the influence of blur on depth prediction observing model
uncertainty with a Bayesian neural network approach. From these studies, we
show that out-of-focus blur greatly improves the depth-prediction network
performances. Furthermore, we transfer the ability learned on a synthetic,
indoor dataset to real, indoor and outdoor images. For this purpose, we present
a new dataset containing real all-focus and defocused images from a Digital
Single-Lens Reflex (DSLR) camera, paired with ground truth depth maps obtained
with an active 3D sensor for indoor scenes. The proposed approach is
successfully validated on both this new dataset and standard ones as NYUv2 or
Depth-in-the-Wild. Code and new datasets are available at
this https URL

本文提出了一种利用深度学习方法和从焦外模糊度生成深度图像的技术来预测在野外环境下的单张图像深度的系统，并通过实验证明，相较于清晰图像，焦外特征能够有效提升深度预测准确性。