The role of machine intelligence in photogrammetric 3D modeling - an overview and perspectives
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Author / Producer
Date
2021
Publication Type
Review Article
ETH Bibliography
yes
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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.
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Publication status
published
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Editor
Book title
Journal / series
Volume
14 (1)
Pages / Article No.
15 - 31
Publisher
Taylor & Francis
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Photogrammetry; camera calibration; 3D modeling; machine learning; object recognition; semantic interpretation