MIDLANDS STATE UNIVERSITY
FACULTY OF SCIENCE AND TECHNOLOGY
DEPARTMENT OF SURVEYING AND GEOMATICS
DIGITAL PHOTOGRAMMETRY
ASSIGNMENT 1
STUDENT NAMES : JASON TULIPOHAMBA B
STUDENT ID : R182433C
LEVEL : 3.2
MODULE CODE : SVG 411
LETURE NAME : MR MLAMBO
DUE DATE: 29 JANUARY 2021
TOPIC: DISCUSS HOW RECENT DEVELOPMENTS IN 3D COMPUTER VISION HAVE
REVOLUTIONISED MODERN DIGITAL PHOTOGRAMMETRY.
, 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 technologies learning techniques in the field of
photogrammetry, as well as its related field computer vision. As a result, the number of new
applications using photogrammetric technologies has increased significantly. Apart from this,
there are areas where 3D computer vision has revolutionised and worth a mention: for example,
earth system modelling, coastal monitoring, agriculture, topographic mapping as well as cultural
heritage (Lichti, et al., 2002).
As a matter of fact, the 3D data on forest structure have transformed the level of detail and
accuracy of forest information. While these 3D data have primarily been derived from airborne
laser scanning, there has been growing interest in the use of 3D data derived from digital aerial
photogrammetry and image matching algorithms, Hartley (1993). According to Gruen (1993),
since the new generation of software tools has spread due to improvement in photogrammetric
technologies, so the techniques of measuring and modelling of 3D based on images gain interest
in photogrammetry over the period of recent years. In fact, the implementation in different
algorithms developed by computer vision, increasing the automation of the standard
photogrammetric process.
Stephan (2012) stated that, the use of image based approaches for 3D models reconstruction had
enormously increased, and therefore, the introduction of computer vision techniques and
procedures permitted to add a major automation, but, in particular, they allow modelling more
complex objects, only exploiting the algorithms implemented in the common software tools.
Shashua & Werman, (1995) suggested that, the computer vision derived dense matching methods
allow obtaining major performance in the presentation of the results, since they permit the
automatic extraction of the position of millions of points, so the geometry of the acquired objects
is reconstructed in greater details.
As stated by Andeu & Serrano (2019), the use of images for measuring 3D features, made many
software to have a wide success in many application fields, including cultural heritage
documentation and analysis. In fact, they offer the possibility of a cheap acquisition, fast and
automatic processing and high accuracies in the results. After the scepticism are past, the
necessity arise for photogrammetrists and geomatics users to know more about the behaviour of
1
FACULTY OF SCIENCE AND TECHNOLOGY
DEPARTMENT OF SURVEYING AND GEOMATICS
DIGITAL PHOTOGRAMMETRY
ASSIGNMENT 1
STUDENT NAMES : JASON TULIPOHAMBA B
STUDENT ID : R182433C
LEVEL : 3.2
MODULE CODE : SVG 411
LETURE NAME : MR MLAMBO
DUE DATE: 29 JANUARY 2021
TOPIC: DISCUSS HOW RECENT DEVELOPMENTS IN 3D COMPUTER VISION HAVE
REVOLUTIONISED MODERN DIGITAL PHOTOGRAMMETRY.
, 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 technologies learning techniques in the field of
photogrammetry, as well as its related field computer vision. As a result, the number of new
applications using photogrammetric technologies has increased significantly. Apart from this,
there are areas where 3D computer vision has revolutionised and worth a mention: for example,
earth system modelling, coastal monitoring, agriculture, topographic mapping as well as cultural
heritage (Lichti, et al., 2002).
As a matter of fact, the 3D data on forest structure have transformed the level of detail and
accuracy of forest information. While these 3D data have primarily been derived from airborne
laser scanning, there has been growing interest in the use of 3D data derived from digital aerial
photogrammetry and image matching algorithms, Hartley (1993). According to Gruen (1993),
since the new generation of software tools has spread due to improvement in photogrammetric
technologies, so the techniques of measuring and modelling of 3D based on images gain interest
in photogrammetry over the period of recent years. In fact, the implementation in different
algorithms developed by computer vision, increasing the automation of the standard
photogrammetric process.
Stephan (2012) stated that, the use of image based approaches for 3D models reconstruction had
enormously increased, and therefore, the introduction of computer vision techniques and
procedures permitted to add a major automation, but, in particular, they allow modelling more
complex objects, only exploiting the algorithms implemented in the common software tools.
Shashua & Werman, (1995) suggested that, the computer vision derived dense matching methods
allow obtaining major performance in the presentation of the results, since they permit the
automatic extraction of the position of millions of points, so the geometry of the acquired objects
is reconstructed in greater details.
As stated by Andeu & Serrano (2019), the use of images for measuring 3D features, made many
software to have a wide success in many application fields, including cultural heritage
documentation and analysis. In fact, they offer the possibility of a cheap acquisition, fast and
automatic processing and high accuracies in the results. After the scepticism are past, the
necessity arise for photogrammetrists and geomatics users to know more about the behaviour of
1
