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.Whil...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)georeferencing/interest point matching(3)Digital Surface Model generation(4)semantic interpretations,followed by conclusions and our insights.展开更多
基金supported by the Office of Naval Research[Award No.N000141712928].
文摘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)georeferencing/interest point matching(3)Digital Surface Model generation(4)semantic interpretations,followed by conclusions and our insights.
