Image alignment and image restoration are classical computer vision tasks.
However, there is still a lack of datasets that provide enough data to train
and evaluate end-to-end deep learning models. Obtaining ground-truth data for
image alignment requires sophisticated structure-from-motion methods or optical
flow systems that often do not provide enough data variance, i.e., typically
providing a high number of image correspondences, while only introducing few
changes of scenery within the underlying image sequences. Alternative
approaches utilize random perspective distortions on existing image data.
However, this only provides trivial distortions, lacking the complexity and
variance of real-world scenarios. Instead, our proposed data augmentation helps
to overcome the issue of data scarcity by using 3D rendering: images are added
as textures onto a plane, then varying lighting conditions, shadows, and
occlusions are added to the scene. The scene is rendered from multiple
viewpoints, generating perspective distortions more consistent with real-world
scenarios, with homographies closely resembling those of camera projections
rather than randomized homographies. For each scene, we provide a sequence of
distorted images with corresponding occlusion masks, homographies, and
ground-truth labels. The resulting dataset can serve as a training and
evaluation set for a multitude of tasks involving image alignment and artifact
removal, such as deep homography estimation, dense image matching, 2D bundle
adjustment, inpainting, shadow removal, denoising, content retrieval, and
background subtraction. Our data generation pipeline is customizable and can be
applied to any existing dataset, serving as a data augmentation to further
improve the feature learning of any existing method.

该研究提出了一种使用 3D 渲染的数据增强方法，以产生更接近于实际世界情境的图像失真。这种数据增强方法可用于训练和评估图像对齐和伪影去除等多种任务，并且可应用于任何现有的数据集以改进任何现有方法的特征学习能力。

SIDAR: 用于对齐与修复的合成图像数据集

SIDAR: Synthetic Image Dataset for Alignment & Restoration

Printed circuit boards (PCBs) are essential components of electronic devices,
and ensuring their quality is crucial in their production. However, the vast
variety of components and PCBs manufactured by different companies makes it
challenging to adapt to production lines with speed demands. To address this
challenge, we present a multi-view object detection framework that offers a
fast and precise solution. We introduce a novel multi-view dataset with
semi-automatic ground-truth data, which results in significant labeling
resource savings. Labeling PCB boards for object detection is a challenging
task due to the high density of components and the small size of the objects,
which makes it difficult to identify and label them accurately. By training an
object detector model with multi-view data, we achieve improved performance
over single-view images. To further enhance the accuracy, we develop a
multi-view inference method that aggregates results from different viewpoints.
Our experiments demonstrate a 15% improvement in mAP for detecting components
that range in size from 0.5 to 27.0 mm.

本文提出了一种多视图目标检测框架，通过使用一个新颖的多视图数据集训练目标检测模型，并开发了多视图推理方法，可以在检测尺寸从 0.5 到 27.0mm 的元件方面提高 15％性能，大大节约地面真实数据标注的资源。

利用多视角数据提高检测性能：一个工业应用案例

Leveraging Multi-view Data for Improved Detection Performance: An  Industrial Use Case

We present a benchmark suite for visual perception. The benchmark is based on
more than 250K high-resolution video frames, all annotated with ground-truth
data for both low-level and high-level vision tasks, including optical flow,
semantic instance segmentation, object detection and tracking, object-level 3D
scene layout, and visual odometry. Ground-truth data for all tasks is available
for every frame. The data was collected while driving, riding, and walking a
total of 184 kilometers in diverse ambient conditions in a realistic virtual
world. To create the benchmark, we have developed a new approach to collecting
ground-truth data from simulated worlds without access to their source code or
content. We conduct statistical analyses that show that the composition of the
scenes in the benchmark closely matches the composition of corresponding
physical environments. The realism of the collected data is further validated
via perceptual experiments. We analyze the performance of state-of-the-art
methods for multiple tasks, providing reference baselines and highlighting
challenges for future research. The supplementary video can be viewed at
this https URL

该研究提出了一个基于虚拟世界的视觉感知基准测试套件，通过对超过 250,000 个高分辨率视频帧的低层次和高层次的视觉任务进行全面地注释，并针对多个任务分析了最先进方法的表现，提供参考基线和突出未来研究的挑战。