ABSTRACT

The process of modern photogrammetry converts images and/or LiDAR data into usable 2D/3D/4D products. The photogrammetric industry offers engineering-grade hardware and software components for various applications. While some components of the data processing pipeline work already automatically, there is still substantial manual involvement required in order to obtain reliable and high-quality results. The recent development of machine learning techniques has attracted a great attention in its potential to address complex tasks that traditionally require manual inputs. It is therefore worth revisiting the role and existing efforts of machine learning techniques in the field of photogrammetry, as well as its neighboring field computer vision. This paper provides an overview of the state-of-the-art efforts in machine learning in bringing the automated and ‘intelligent’ component to photogrammetry, computer vision and (to a lesser degree) to remote sensing. We will primarily cover the relevant efforts following a typical 3D photogrammetric processing pipeline: (1) data acquisition (2) geo-referencing/interest point matching (3) Digital Surface Model generation (4) semantic interpretations, followed by conclusions and our insights.

摘要

现代摄影测量技术的过程将图像和/或LiDAR数据转换为可用的2D / 3D / 4D产品。摄影测量行业为各种应用提供工程级的硬件和软件组件。尽管数据处理流水线的某些组件已经自动运行，但是仍然需要大量的人工干预才能获得可靠和高质量的结果。机器学习技术的最新发展因其解决传统上需要人工输入的复杂任务的潜力而备受关注。因此，值得回顾一下机器学习技术在摄影测量学及其邻近领域的计算机视觉领域中的作用和现有的努力。本文概述了机器学习中将自动和“智能”组件引入摄影测量，计算机视觉和（在较小程度上）遥感领域的最新技术成果。我们将主要按照典型的3D摄影测量处理流程介绍相关工作：（1）数据获取（2）地理参考/兴趣点匹配（3）数字表面模型生成（4）语义解释，然后给出结论和我们的见识。