Current methods for single-image depth estimation use training datasets with
real image-depth pairs or stereo pairs, which are not easy to acquire. We
propose a framework, trained on synthetic image-depth pairs and unpaired real
images, that comprises an image translation network for enhancing realism of
input images, followed by a depth prediction network. A key idea is having the
first network act as a wide-spectrum input translator, taking in either
synthetic or real images, and ideally producing minimally modified realistic
images. This is done via a reconstruction loss when the training input is real,
and GAN loss when synthetic, removing the need for heuristic
self-regularization. The second network is trained on a task loss for synthetic
image-depth pairs, with extra GAN loss to unify real and synthetic feature
distributions. Importantly, the framework can be trained end-to-end, leading to
good results, even surpassing early deep-learning methods that use real paired
data.

本文提出一种基于图像翻译网络和深度预测网络的框架，使用合成的图像 - 深度对和非配对的真实图像进行训练，实现单图深度估计。该框架可以端到端训练，得到好的结果，甚至超过使用真实配对数据的早期深度学习方法。

T2Net: 用于解决单图像深度估计任务的从合成到逼真的翻译

T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth  Estimation Tasks

While an increasing interest in deep models for single-image depth estimation
methods can be observed, established schemes for their evaluation are still
limited. We propose a set of novel quality criteria, allowing for a more
detailed analysis by focusing on specific characteristics of depth maps. In
particular, we address the preservation of edges and planar regions, depth
consistency, and absolute distance accuracy. In order to employ these metrics
to evaluate and compare state-of-the-art single-image depth estimation
approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera
together with a laser scanner to acquire high-resolution images and highly
accurate depth maps. Experimental results show the validity of our proposed
evaluation protocol.

本文提出了一组新的质量标准，允许更详细地分析深度图的特定特征，以便评估最先进的单图像深度估计方法，并提供了一个新的高质量 RGB-D 数据集。实验结果显示了我们提出的评估协议的有效性。

基于 CNN 的单张图片深度估计方法评估

Evaluation of CNN-based Single-Image Depth Estimation Methods

We study the problem of single-image depth estimation for images in the wild.
We collect human annotated surface normals and use them to train a neural
network that directly predicts pixel-wise depth. We propose two novel loss
functions for training with surface normal annotations. Experiments on NYU
Depth and our own dataset demonstrate that our approach can significantly
improve the quality of depth estimation in the wild.

该研究探讨了针对野外图像的单幅图像深度估计问题。研究使用人工标注表面法线进行训练，并使用两个新颖的损失函数进行训练。研究表明，该方法可以显著提高野外深度估计质量。