Existing calibration methods occasionally fail for large field-of-view
cameras due to the non-linearity of the underlying problem and the lack of good
initial values for all parameters of the used camera model. This might occur
because a simpler projection model is assumed in an initial step, or a poor
initial guess for the internal parameters is pre-defined. A lot of the
difficulties of general camera calibration lie in the use of a forward
projection model. We side-step these challenges by first proposing a solver to
calibrate the parameters in terms of a back-projection model and then regress
the parameters for a target forward model. These steps are incorporated in a
robust estimation framework to cope with outlying detections. Extensive
experiments demonstrate that our approach is very reliable and returns the most
accurate calibration parameters as measured on the downstream task of absolute
pose estimation on test sets. The code is released at
this https URL

提出了一种相机标定方法，通过后方投影模型先校准参数，然后回归到前方投影模型中，最终在绝对姿态估计任务上得出最准确的标定参数。

BabelCalib: 一种万能的中央摄像机标定方法

BabelCalib: A Universal Approach to Calibrating Central Cameras

We proposed a novel architecture for the problem of video super-resolution.
We integrate spatial and temporal contexts from continuous video frames using a
recurrent encoder-decoder module, that fuses multi-frame information with the
more traditional, single frame super-resolution path for the target frame. In
contrast to most prior work where frames are pooled together by stacking or
warping, our model, the Recurrent Back-Projection Network (RBPN) treats each
context frame as a separate source of information. These sources are combined
in an iterative refinement framework inspired by the idea of back-projection in
multiple-image super-resolution. This is aided by explicitly representing
estimated inter-frame motion with respect to the target, rather than explicitly
aligning frames. We propose a new video super-resolution benchmark, allowing
evaluation at a larger scale and considering videos in different motion
regimes. Experimental results demonstrate that our RBPN is superior to existing
methods on several datasets.

提出了一种新颖的视频超分辨率方法，利用具有循环编码器 - 解码器模块的迭代背投影网络从连续视频帧中集成时空上下文信息，通过迭代反向投影框架，无需将帧堆叠在一起，实现对目标帧的高分辨率重建。

视频超分辨率的循环反投影网络

Recurrent Back-Projection Network for Video Super-Resolution

In this work, we demonstrate that 3D poses in video can be effectively
estimated with a fully convolutional model based on dilated temporal
convolutions over 2D keypoints. We also introduce back-projection, a simple and
effective semi-supervised training method that leverages unlabeled video data.
We start with predicted 2D keypoints for unlabeled video, then estimate 3D
poses and finally back-project to the input 2D keypoints. In the supervised
setting, our fully-convolutional model outperforms the previous best result
from the literature by 6 mm mean per-joint position error on Human3.6M,
corresponding to an error reduction of 11%, and the model also shows
significant improvements on HumanEva-I. Moreover, experiments with
back-projection show that it comfortably outperforms previous state-of-the-art
results in semi-supervised settings where labeled data is scarce. Code and
models are available at this https URL

本文提出了一种基于卷积神经网络的 3D 姿势估计方法，并结合反向投影技术实现了半监督训练，结果表明该模型在无监督和半监督情况下准确性都得到了明显提高。

使用时域卷积和半监督训练进行视频中三维人体姿态估计

3D human pose estimation in video with temporal convolutions and  semi-supervised training

The feed-forward architectures of recently proposed deep super-resolution
networks learn representations of low-resolution inputs, and the non-linear
mapping from those to high-resolution output. However, this approach does not
fully address the mutual dependencies of low- and high-resolution images. We
propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and
down-sampling layers, providing an error feedback mechanism for projection
errors at each stage. We construct mutually-connected up- and down-sampling
stages each of which represents different types of image degradation and
high-resolution components. We show that extending this idea to allow
concatenation of features across up- and down-sampling stages (Dense DBPN)
allows us to reconstruct further improve super-resolution, yielding superior
results and in particular establishing new state of the art results for large
scaling factors such as 8x across multiple data sets.

该研究提出了使用深度反投影网络进行超分辨率图像重建的方法，利用迭代升采样和降采样，同时考虑低分辨率输入与高分辨率输出之间的相互依赖关系，并构建了互相连接的上采样和下采样阶段来表示不同类型的图像退化和高分辨率特征，结果证明该方法在多个数据集上实现了新的最佳结果，特别是针对大比例尺的情况。