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按照最简易安装构建失败了好几次

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Method

In this section, we describe our unsupervised framework for monocular depth estimation. We first review the self-supervised training pipeline for monocular depth estimation, and then introduce the co-attention module and pose graph consistency loss function.

Supervision from Image Reconstruction

Following the formulation in \cite{zhou_unsupervised_2017}, the whole framework includes a DispNet and a PoseNet, the DispNet produces depth map and the PoseNet produces the relative pose between two RGB frames.

Given a sequence of consecutive frames $X_{t-1}, X_t$ and $X_{t+1}$，we estimate the depth for each frame, and the relative pose for every two adjacent frames, then we get depth map $D_{t-1}, D_t, D_{t+1}$ and translation matrix $T_{t-1\rightarrow t}, T_{t\rightarrow t+1}$.

Consider the adjacent frame pair $I_t$ and $I_{t+1}$, once the estimated depth $D_t$ and translation matrix $T_{t\rightarrow t+1}$ are available, we can project the source image $I_t$ to the next moment

$$
p(\hat{I}{t+1}) = KT{t\rightarrow t+1}D_tK^{-1}p(I_t)
$$

the function $p(.)$ denotes sampling from the homogeneous coordinates of image and $K$ denotes the camera insrinsic matrix, $\hat{I}_{t+1}$ can be reconstucted using the differentiable sampling mechanism proposed in \cite{jaderberg_spatial_2015}.

Hence the problem is formulated to the minimization of a phtometric reprojection error $L_p$

$$
L_p = \alpha \left|I_{t+1} - \hat{I}{t+1}\right|1 + (1 - \alpha)SSIM(I{t+1}, \hat{I}{t+1})
$$

$SSIM(.)$ is the structural similarity\cite{wang_image_2004} loss for evaluating the quality of image predictions, and to regularize the depth, we use a disparity image smoothness constraint as widely used in previous work\cite{mahjourian_unsupervised_2018,zhou_unsupervised_2017,garg_unsupervised_2016}

$$ L_{\mathrm{s}}=\sum_{x, y}\left|\partial_{x} D_{t}\right| e^{-\left|\partial_{x} I_{t}\right|}+\left|\partial_{y} D_{t}\right| e^{-\left|\partial_{y} I_{t}\right|} $$

List

Here is a list:

• Xue Bai, Jue Wang, David Simons, and Guillermo Sapiro.Video SnapCut: robust video object cutout using localized classifiers. TOG, 28(3):70, 2009.
• Linchao Bao, Baoyuan Wu, and Wei Liu. CNN in MRF: Video object segmentation via inference in a CNN-based higher-order spatio-temporal MRF. In CVPR, 2018

Code

Here is some code:

def bi_search(arr:list, x:int):
  l, r = 0, len(arr)
  while l < r:
    m = (l + r) >> 1
    if arr[m] >= x: r = m
    else: l = m + 1
  return l

Image

Table

🔥🔥🔥

我按照最简安装在 junqi-lu/junqi-lu.github.io 下新建了三个文件，可以正常的运行github action，也都运行成功了，但是部署失败了。我不太懂vuepress和github action，但感觉理想情况下应该是在这个仓库下新建一系列静态渲染好的文件？

此外github现在默认分支名叫做main了，所以我把配置文件中的部分master换成main了。不清楚是否是因为这个。

Move from Yidadaa/Yidadaa.github.io#32

How to post on this blog site.?