解读Depth Map Prediction from a Single Image using a Multi-Scale Deep Network （1）
我不会把这些翻译成中文，一是麻烦，二是因为英语是无法回避的！
先来看一下摘要，可以得到一些信息：
(1) Predicting depth is an essential component in understanding the 3D geometry of a scene
(2) A new method is presented to find depth relations from a single view by employing two
deep network stacks:
a. makes a coarse global prediction based on the entire image
b. refines this prediction locally
(3) apply a scale-invariant error to help measure depth relations
再来看看Inroduction和Related work,可以得到前人在做深度估计的成就
(1)Provided accurate image correspondences, depth can be recovered deterministically in the stereo
case [5]
(2) predict depth from a set of image features using linear regression and a MRF [15]
(3) integrate semantic object labels with monocular depth features to improve performance [12]
(4) use a KNN transfer mechanism based on SIFT Flow to estimate depths of static backgrounds
from single images, which they augment with motion information to better estimate moving
foreground subjects in video [7, 11]
当然，我的摘抄分析不一定完全。从他人成就来看，用CNN处理单目深度估计的思想是超前的
来看看他们怎么叙述自己的Approach，
从图1（Model architecture），很容易了解CNN网络处理图片的过程
如果你像我一样不明把conv,stride,pool的含义，可以参考这篇博文点击打开链接
学习这篇博文中。。。。。。