In three-dimensional models obtained by photogrammetry of existing
structures, all of the shapes that the eye can select cannot always find their
equivalents in the geometric components of the model. However, the matching of
meaningful parts and assemblages with the records acquired with rapid and
detailed documentation methods will provide an advantage for the creation of
information models of existing structures. While aiming to produce answers to
this problem and in order to overcome the difficulties of pattern recognition
in three-dimensional models, we used two-dimensional samples obtained by
projection. Processing techniques such as ambient occlusion, curvature and
normal maps are commonly used in modern computer graphics applications that
enable the representation of three-dimensional surface properties in
two-dimensional data sets. The method we propose is based on the recognition of
patterns through these mappings instead of the usual light-based visualization.
The first stage of the application is photogrammetric capture of a few examples
of Zeugma mosaics and three-dimensional digital modeling of a set of Seljuk era
brick walls based on knowledge obtained through architectural history
literature. The second stage covers the creation of digital models byprocessing
the surface representation obtained from this data using Alice Vision,
OpenCV-Python, and Autodesk Maya to include information on aspects of the
making of the walls. What is envisioned for the next stages is that the mapping
data contributes and supports the knowledge for rule-based design and making
processesof cultural heritage.

使用摄影测量学、三维模型、模式识别、表面表示和文化遗产，提出了一种新的基于映射的文化遗产设计和制作方法。

嵌入式形状匹配在摄影测量数据中的应用于建模知识

Embedded Shape Matching in Photogrammetry Data for Modeling Making  Knowledge

In this work we propose a novel, highly practical, binocular photometric
stereo (PS) framework, which has same acquisition speed as single view PS,
however significantly improves the quality of the estimated geometry.
As in recent neural multi-view shape estimation frameworks such as NeRF,
SIREN and inverse graphics approaches to multi-view photometric stereo (e.g.
PS-NeRF) we formulate shape estimation task as learning of a differentiable
surface and texture representation by minimising surface normal discrepancy for
normals estimated from multiple varying light images for two views as well as
discrepancy between rendered surface intensity and observed images. Our method
differs from typical multi-view shape estimation approaches in two key ways.
First, our surface is represented not as a volume but as a neural heightmap
where heights of points on a surface are computed by a deep neural network.
Second, instead of predicting an average intensity as PS-NeRF or introducing
lambertian material assumptions as Guo et al., we use a learnt BRDF and perform
near-field per point intensity rendering.
Our method achieves the state-of-the-art performance on the DiLiGenT-MV
dataset adapted to binocular stereo setup as well as a new binocular
photometric stereo dataset - LUCES-ST.

提出了一种新颖、高度实用的双目光度立体（PS）框架，其采集速度与单视图 PS 相同，但显著改善了几何估计的质量。和最近的神经多视图形状估计方法（如 NeRF、SIREN 和多视图光度立体的反向图形方法）一样，我们将形状估计任务定义为通过最小化多个变化的光照图像估计的法线的表面法线差异，以及渲染表面强度与观察图像之间的差异，学习可微表面和纹理表示。我们的方法与典型的多视图形状估计方法有两个关键区别。首先，我们的表面不是用体积表示，而是用深度神经网络计算表面上各点的高度的神经高度图来表示。其次，我们使用了学习的 BRDF 并进行了点接近场强度渲染，而不是像 Guo 等人一样预测平均强度或引入兰伯特材料假设。我们的方法在针对双目立体设置进行适应的 DiLiGenT-MV 数据集以及新的双目光度立体数据集 - LUCES-ST 上取得了最先进的性能。

双目光度立体摄影技术中的神经高度图方法

A Neural Height-Map Approach for the Binocular Photometric Stereo  Problem

The ability to perform arithmetic tasks is a remarkable trait of human
intelligence and might form a critical component of more complex reasoning
tasks. In this work, we investigate if the surface form of a number has any
influence on how sequence-to-sequence language models learn simple arithmetic
tasks such as addition and subtraction across a wide range of values. We find
that how a number is represented in its surface form has a strong influence on
the model's accuracy. In particular, the model fails to learn addition of
five-digit numbers when using subwords (e.g., "32"), and it struggles to learn
with character-level representations (e.g., "3 2"). By introducing position
tokens (e.g., "3 10e1 2"), the model learns to accurately add and subtract
numbers up to 60 digits. We conclude that modern pretrained language models can
easily learn arithmetic from very few examples, as long as we use the proper
surface representation. This result bolsters evidence that subword tokenizers
and positional encodings are components in current transformer designs that
might need improvement. Moreover, we show that regardless of the number of
parameters and training examples, models cannot learn addition rules that are
independent of the length of the numbers seen during training. Code to
reproduce our experiments is available at
this https URL

研究了表现形式对于序列到序列的语言模型学习加减法算术任务的影响，发现表现形式对模型的准确性有很大影响；通过引入位置标记，模型可以准确地学习添加和减少 60 位数字。无论参数和训练示例数量如何，模型都不能学习与训练期间数字长度无关的加和规则。

通过简单的算术任务研究变形金刚的限制

Investigating the Limitations of Transformers with Simple Arithmetic  Tasks

The three-dimensional reconstruction of scenes from multiple views has made
impressive strides in recent years, chiefly by methods correlating isolated
feature points, intensities, or curvilinear structure. In the general setting,
i.e., without requiring controlled acquisition, limited number of objects,
abundant patterns on objects, or object curves to follow particular models, the
majority of these methods produce unorganized point clouds, meshes, or voxel
representations of the reconstructed scene, with some exceptions producing 3D
drawings as networks of curves. Many applications, e.g., robotics, urban
planning, industrial design, and hard surface modeling, however, require
structured representations which make explicit 3D curves, surfaces, and their
spatial relationships. Reconstructing surface representations can now be
constrained by the 3D drawing acting like a scaffold to hang on the computed
representations, leading to increased robustness and quality of reconstruction.
This paper presents one way of completing such 3D drawings with surface
reconstructions, by exploring occlusion reasoning through lofting algorithms.

通过挖掘遮挡推理的方式，运用曲面拟合算法，将 3D 绘制曲线转化为曲面，以构建有结构的 3D 重建场景。