In this paper, we develop a modified differential Structure from Motion (SfM)
algorithm that can estimate relative pose from two consecutive frames despite
of Rolling Shutter (RS) artifacts. In particular, we show that under constant
velocity assumption, the errors induced by the rolling shutter effect can be
easily rectified by a linear scaling operation on each optical flow. We further
propose a 9-point algorithm to recover the relative pose of a rolling shutter
camera that undergoes constant acceleration motion. We demonstrate that the
dense depth maps recovered from the relative pose of the RS camera can be used
in a RS-aware warping for image rectification to recover high-quality Global
Shutter (GS) images. Experiments on both synthetic and real RS images show that
our RS-aware differential SfM algorithm produces more accurate results on
relative pose estimation and 3D reconstruction from images distorted by RS
effect compared to standard SfM algorithms that assume a GS camera model. We
also demonstrate that our RS-aware warping for image rectification method
outperforms state-of-the-art commercial software products, i.e. Adobe After
Effects and Apple Imovie, at removing RS artifacts.

本文提出了一种改进的运动结构法（SfM）算法，该算法考虑了无论 Rolling Shutter（RS）的影响，都能够估计两个连续帧的相对姿态，并给出了用于复原 Rolling Shutter 相机的相对姿态的 9 点算法，使用该算法的图像矫正可产生高质量的全局快门（GS）图像和 3D 重建结果。

快门滚动感知差分 SfM 和图像矫正

Rolling-Shutter-Aware Differential SfM and Image Rectification

Estimating the relative rigid pose between two RGB-D scans of the same
underlying environment is a fundamental problem in computer vision, robotics,
and computer graphics. Most existing approaches allow only limited maximum
relative pose changes since they require considerable overlap between the input
scans. We introduce a novel deep neural network that extends the scope to
extreme relative poses, with little or even no overlap between the input scans.
The key idea is to infer more complete scene information about the underlying
environment and match on the completed scans. In particular, instead of only
performing scene completion from each individual scan, our approach alternates
between relative pose estimation and scene completion. This allows us to
perform scene completion by utilizing information from both input scans at late
iterations, resulting in better results for both scene completion and relative
pose estimation. Experimental results on benchmark datasets show that our
approach leads to considerable improvements over state-of-the-art approaches
for relative pose estimation. In particular, our approach provides encouraging
relative pose estimates even between non-overlapping scans.

提出了一种使用深度神经网络处理 RGB-D 扫描数据的方法，该方法可以处理极端的相对位姿变换，且在相对位姿估计和场景完成方面都达到了更好的效果。

基于场景补全的 RGB-D 扫描极端相对姿态估计

Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion

This paper presents a convolutional neural network based approach for
estimating the relative pose between two cameras. The proposed network takes
RGB images from both cameras as input and directly produces the relative
rotation and translation as output. The system is trained in an end-to-end
manner utilising transfer learning from a large scale classification dataset.
The introduced approach is compared with widely used local feature based
methods (SURF, ORB) and the results indicate a clear improvement over the
baseline. In addition, a variant of the proposed architecture containing a
spatial pyramid pooling (SPP) layer is evaluated and shown to further improve
the performance.

本文提出了一种基于卷积神经网络的方法来估计两个相机之间的相对姿态，该方法采用来自两个相机的 RGB 图像作为输入，并直接输出相对旋转和平移。与 SURF、ORB 这些广泛使用的局部特征方法相比较，该方法采用端到端的方式训练，利用大规模分类数据集进行迁移学习，结果表明相对基准线有明显的改进。此外，本文还介绍了一种包含空间金字塔池化 (SPP) 层的网络结构变体，并证明进一步提高了性能。