Document dewarping, aiming to eliminate geometric deformation in photographed
documents to benefit text recognition, has made great progress in recent years
but is still far from being solved. While Cartesian coordinates are typically
leveraged by state-of-the-art approaches to learn a group of deformation
control points, such representation is not efficient for dewarping model to
learn the deformation information. In this work, we explore Polar coordinates
representation for each point in document dewarping, namely Polar-Doc. In
contrast to most current works adopting a two-stage pipeline typically, Polar
representation enables a unified point regression framework for both
segmentation and dewarping network in one single stage. Such unification makes
the whole model more efficient to learn under an end-to-end optimization
pipeline, and also obtains a compact representation. Furthermore, we propose a
novel multi-scope Polar-Doc-IOU loss to constrain the relationship among
control points as a grid-based regularization under the Polar representation.
Visual comparisons and quantitative experiments on two benchmarks show that,
with much fewer parameters than the other mainstream counterparts, our
one-stage model with multi-scope constraints achieves new state-of-the-art
performance on both pixel alignment metrics and OCR metrics. Source codes will
be available at \url{*****}.

本文提出了一种使用极坐标表示（Polar-Doc）的文档平整化方法，将分割和去变形网络统一到单个阶段中进行点回归框架的训练，在像素对齐度量和 OCR 度量方面取得了新的最优表现。

Polar-Doc: 极坐标约束下的多尺度一阶段文档矫正

Polar-Doc: One-Stage Document Dewarping with Multi-Scope Constraints  under Polar Representation

Translational invariance induced by pooling operations is an inherent
property of convolutional neural networks, which facilitates numerous computer
vision tasks such as classification. Yet to leverage rotational invariant
tasks, convolutional architectures require specific rotational invariant layers
or extensive data augmentation to learn from diverse rotated versions of a
given spatial configuration. Unwrapping the image into its polar coordinates
provides a more explicit representation to train a convolutional architecture
as the rotational invariance becomes translational, hence the visually distinct
but otherwise equivalent rotated versions of a given scene can be learnt from a
single image. We show with two common vision-based solar irradiance forecasting
challenges (i.e. using ground-taken sky images or satellite images), that this
preprocessing step significantly improves prediction results by standardising
the scene representation, while decreasing training time by a factor of 4
compared to augmenting data with rotations. In addition, this transformation
magnifies the area surrounding the centre of the rotation, leading to more
accurate short-term irradiance predictions.

对图像进行极坐标变换后，利用卷积神经网络进行太阳辐射预测比进行数据旋转增广的方法具有更高的预测准确率和更短的训练时间。

SPIN: 神经网络极性不变性简化应用于基于视觉的辐照度预测

SPIN: Simplifying Polar Invariance for Neural networks Application to  vision-based irradiance forecasting

Reducing the complexity of the pipeline of instance segmentation is crucial
for real-world applications. This work addresses this issue by introducing an
anchor-box free and single-shot instance segmentation framework, termed
PolarMask, which reformulates the instance segmentation problem as predicting
the contours of objects in the polar coordinate, with several appealing
benefits. (1) The polar representation unifies instance segmentation (masks)
and object detection (bounding boxes) into a single framework, reducing the
design and computational complexity. (2) Two modules are carefully designed
(i.e. soft polar centerness and polar IoU loss) to sample high-quality center
examples and optimize polar contour regression, making the performance of
PolarMask does not depend on the bounding box prediction results and thus
becomes more efficient in training. (3) PolarMask is fully convolutional and
can be easily embedded into most off-the-shelf detection methods. To further
improve the accuracy of the framework, a Refined Feature Pyramid is introduced
to further improve the feature representation at different scales, termed
PolarMask++. Extensive experiments demonstrate the effectiveness of both
PolarMask and PolarMask++, which achieve competitive results on instance
segmentation in the challenging COCO dataset with single-model and single-scale
training and testing, as well as new state-of-the-art results on rotate text
detection and cell segmentation. We hope the proposed polar representation can
provide a new perspective for designing algorithms to solve single-shot
instance segmentation. The codes and models are available at:
github.com/xieenze/PolarMask.

本研究提出了一种基于极坐标的无锚盒实例分割框架，将实例分割问题重构为在极坐标下预测物体轮廓，具有统一的实例分割和物体检测框架、计算复杂度低的优点，可以轻松嵌入已有的检测方法中。另外还使用了细化的特征金字塔模型对算法进行改进，实验表明这种方法在处理实例分割问题方面表现具有竞争力。