Surface reconstruction for point clouds is an important task in 3D computer
vision. Most of the latest methods resolve this problem by learning signed
distance functions (SDF) from point clouds, which are limited to reconstructing
shapes or scenes with closed surfaces. Some other methods tried to represent
shapes or scenes with open surfaces using unsigned distance functions (UDF)
which are learned from large scale ground truth unsigned distances. However,
the learned UDF is hard to provide smooth distance fields near the surface due
to the noncontinuous character of point clouds. In this paper, we propose a
novel method to learn consistency-aware unsigned distance functions directly
from raw point clouds. We achieve this by learning to move 3D queries to reach
the surface with a field consistency constraint, where we also enable to
progressively estimate a more accurate surface. Specifically, we train a neural
network to gradually infer the relationship between 3D queries and the
approximated surface by searching for the moving target of queries in a dynamic
way, which results in a consistent field around the surface. Meanwhile, we
introduce a polygonization algorithm to extract surfaces directly from the
gradient field of the learned UDF. The experimental results in surface
reconstruction for synthetic and real scan data show significant improvements
over the state-of-the-art under the widely used benchmarks.

本研究提出了一种从原始点云直接学习一致性感知无符号距离函数的新方法，以进行表面重建，同时引入了多边形化算法以直接从学习的 UDF 的梯度场中提取表面。实验结果表明，在合成和真实扫描数据的表面重建方面，该方法在广泛使用的基准测试中明显优于现有技术。

从原始点云逐步学习具有一致性感知的无符号距离函数

Learning Consistency-Aware Unsigned Distance Functions Progressively  from Raw Point Clouds

Layout Analysis (the identification of zones and their classification) is the
first step along line segmentation in Optical Character Recognition and similar
tasks. The ability of identifying main body of text from marginal text or
running titles makes the difference between extracting the work full text of a
digitized book and noisy outputs. We show that most segmenters focus on pixel
classification and that polygonization of this output has not been used as a
target for the latest competition on historical document (ICDAR 2017 and
onwards), despite being the focus in the early 2010s. We propose to shift, for
efficiency, the task from a pixel classification-based polygonization to an
object detection using isothetic rectangles. We compare the output of Kraken
and YOLOv5 in terms of segmentation and show that the later severely
outperforms the first on small datasets (1110 samples and below). We release
two datasets for training and evaluation on historical documents as well as a
new package, YALTAi, which injects YOLOv5 in the segmentation pipeline of
Kraken 4.1.

我们提出了使用等面矩形进行对象检测的方法，以替换基于像素分类的多边形化，从而更有效地完成光学字符识别中的版面分析任务。对历史文献进行测试时，使用 YOLOv5 的结果比 Kraken 更为优秀。我们提供了两个历史文献训练和评估数据集，并发布了一个新软件包，YALTAi，它将 YOLOv5 注入到 Kraken 4.1 的分割流程中。