This paper introduces a novel, markerless, step-by-step, in-situ 3D Augmented
Reality (AR) instruction method and its application - BRICKxAR (Multi 3D
Models/M3D) - for small parts assembly. BRICKxAR (M3D) realistically visualizes
rendered 3D assembly parts at the assembly location of the physical assembly
model (Figure 1). The user controls the assembly process through a user
interface. BRICKxAR (M3D) utilizes deep learning-trained 3D model-based
registration. Object recognition and tracking become challenging as the
assembly model updates at each step. Additionally, not every part in a 3D
assembly may be visible to the camera during the assembly. BRICKxAR (M3D)
combines multiple assembly phases with a step count to address these
challenges. Thus, using fewer phases simplifies the complex assembly process
while step count facilitates accurate object recognition and precise
visualization of each step. A testing and heuristic evaluation of the BRICKxAR
(M3D) prototype and qualitative analysis were conducted with users and experts
in visualization and human-computer interaction. Providing robust 3D AR
instructions and allowing the handling of the assembly model, BRICKxAR (M3D)
has the potential to be used at different scales ranging from manufacturing
assembly to construction.

介绍一种新的无标记、分步、现场三维增强现实（AR）指导方法及其应用 - BRICKxAR（Multi 3D Models/M3D），用于小零件组装。通过使用深度学习训练的三维模型注册，BRICKxAR（M3D）能够在物理组装模型的组装位置逼真地可视化渲染的三维组装零件，并通过用户界面控制组装流程，同时结合多个组装阶段和步骤计数的方式来解决物体识别和跟踪的难题，从而简化复杂的组装过程并实现准确的物体识别和精确的可视化展示。通过对 BRICKxAR（M3D）的原型进行测试和启发式评估，并与可视化和人机交互领域的用户和专家进行定性分析，该方法具有提供强大的三维增强现实指导和处理组装模型的潜力，可应用于从制造组装到建筑施工等不同规模的领域。

基于多个 3D 模型的注册的增强现实（AR）装配指导

Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions  for Assembly

Solar Photovoltaic (PV) is increasingly being used to address the global
concern of energy security. However, hot spot and snail trails in PV modules
caused mostly by crakes reduce their efficiency and power capacity. This
article presents a groundbreaking methodology for automatically identifying and
analyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV)
modules, leveraging unsupervised sensing algorithms and 3D Augmented Reality
(AR) visualization. By transforming the traditional methods of diagnosis and
repair, our approach not only enhances efficiency but also substantially cuts
down the cost of PV system maintenance. Validated through computer simulations
and real-world image datasets, the proposed framework accurately identifies
dirty regions, emphasizing the critical role of regular maintenance in
optimizing the power capacity of solar PV modules. Our immediate objective is
to leverage drone technology for real-time, automatic solar panel detection,
significantly boosting the efficacy of PV maintenance. The proposed methodology
could revolutionize solar PV maintenance, enabling swift, precise anomaly
detection without human intervention. This could result in significant cost
savings, heightened energy production, and improved overall performance of
solar PV systems. Moreover, the novel combination of unsupervised sensing
algorithms with 3D AR visualization heralds new opportunities for further
research and development in solar PV maintenance.

本文提出了一种使用无监督传感算法和三维增强现实可视化来自动识别和分析太阳能光伏（PV）电池模块中的异常现象的方法，以增强效率并显著降低 PV 系统维护成本。

揭示不可见之处：使用无监督感知算法和三维增强现实技术增强太阳能光伏电池模块老化区域的检测和分析

Unveiling the invisible: Enhanced detection and analysis deteriorated  areas in solar PV modules using unsupervised sensing algorithms and 3D  augmented reality