We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in graph neural networks (GNNs) and their evolution with explainable artificial intelligence (XAI), and 3D geometric priors with the human‐in‐the‐loop. We follow a simple definition of a “digital twin,” as a high‐precision, three‐dimensional digital representation of a physical object or environment, captured, for example, by Light Detection and Ranging (LiDAR) technology. After a digression into transforming PCD into images, graphs, combinatorial complexes and hypergraphs, we explore recent developments in geometric deep learning (GDL) and provide insight into the application of these network architectures for analyzing and learning from graph‐structured data. We emphasize the importance of the explainability of these models and recognize that the ability to interpret and validate the results of complex models is a crucial aspect of their wider adoption.This article is categorized under: Technologies > Artificial Intelligence

中文翻译：

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从 3D 点云数据到可解释的几何深度学习：最先进的技术和未来的挑战


我们呈现了从 3D 点云数据 (PCD) 到最先进的图神经网络 (GNN) 的激动人心的旅程，以及它们通过可解释的人工智能 (XAI) 的演变，以及人类在场的 3D 几何先验。环形。我们遵循“数字孪生”的简单定义，即通过光探测和测距 (LiDAR) 技术等捕获的物理对象或环境的高精度三维数字表示。在将 PCD 转换为图像、图形、组合复合体和超图之后，我们探讨了几何深度学习 (GDL) 的最新发展，并深入了解这些网络架构在图结构数据分析和学习中的应用。我们强调这些模型的可解释性的重要性，并认识到解释和验证复杂模型结果的能力是其更广泛采用的关键方面。本文分类如下：技术 > 人工智能