- Course
- 2025-2026
- Semester
- 2
- ECTS
- 6
- Type
- Elective
- University
- UVigo
Subject objectives
In this subject students will learn to:
1.- Accurately model an image acquisition system from a geometric point of view;
2.- Model the relative orientation between images and the acquisition and processing methodologies to obtain
a local system 3D model
3.- Select and collect 3D data from different LiDAR sensors
4.- Process and analyze 3D point clouds based on geometric and radiometric information
Contents
- Advanced Calibration of cameras.
- Fundamentals of photogrammetry
- Interior, Relative, Absolute orientation
- Bundle Adjustment, Spatial transformations
- GeoRectification
- 3D Data Acquisition
- Point Cloud data collection
- Point Cloud structure and conversion
- LiDAR data collection and sensor fusion
- 3D Data Analysis and Processing
- Geometric and radiometric analysis
- Visualization
- Machine Learning applied to 3D data
- Advanced Techniques in Spatial Computing
- Mixed Reality in 3D scenarios
- Simulated Point Clouds with HELIOS++
- Visual Odometry and SLAM
- Real Applications and Case Studies
Basic and complementary bibliography
Thomas Luhmann, Close Range Photogrammetry, 1-870325-50-8, Whittles Publishing, 2006
Richard Hartley, Multiple view geometry in Computer Vision, 0521-54051-8, 2, Cambridge : Cambridge University Press, 2003
Karl Kraus, Photogrammetry : geometry from images and laser scans, 978-3-11-019007-6, 2, Berlin ; New York : Walter De Gruyter, cop., 2007
Complementary Bibliography
Wolfgang FörstnerBernhard P. Wrobel, Photogrammetric Computer Vision, 978-3-319-11549-8, Springer, 2016
Competencies
CB6 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context.
CB9. That the students know how to communicate their conclusions –and the ultimate knowledge and reasons that support them– to specialized and non-specialized audiences in a clear and unambiguous way.
CB10: Students are expected to acquire the learning skills that allow them to continue studying in a way that will be largely self-driven or autonomous.
CT2- Capacity for teamwork, organization and planning.
CE1. Know and apply the concepts, methodologies and technologies of image processing.
CE3. Know and apply the concepts, methodologies and technologies of image and video analysis.
CE5: Students are expected to know how to analyze and apply state of the art methods in computer vision.
CE6- To be knowledgeable and to apply fundamentals of image acquisition and computer vision.
CE9: Students are expected to know and apply the concepts, methodologies and technologies for the recognition of visual patterns in real scenes.
Teaching methodology
Lecturing:
It will consist of the collaborative discussion of contents of the course of way.
This includes discussion and solving problems and practical case studies in the classroom.
Practices through ICT:
Methodology oriented to solving cases of study related with the thematic of the course using software of reference.
Practices and exercises focused on the implementation of the algorithms explained in the participatory classes.
This subject requires face-to-face assistance fof all students at the University of Vigo to carry out part of their laboratory practices.
Mentored work:
Taking into account proposed practical case studies, this method is oriented to solving and documenting a complete photogrammetric project, including the definition of: 3D data acquisition methodologies in the field, supporting data processing for modeling the main products obtained through the photogrammetric process .
Seminars:
The description of a concrete practical case related with the professional practice of photogrammetry.
For all the modalities of teaching, tutorial session meetings could be held by telematic means (email, videoconference, forums in FAITIC, …) Under the modality of previous agreement.
Evaluation system
Mentored work (30):
The students will have to complete a case of study by means of the design of a methodology that include the steps seen in the course:
1.- Objectives, Requirements and Products analysis
2.- Definition of the image acquisition networks in the case study
3.- Image processing and analysis
4.- Obtaining key photogrammetric products.
Evaluated Competences:
CB6
CB9
CB10
CT2
CE1
CE3
CE5
CE6
CE9
Objective questions exam (30):
The students will have to answer individually a test with questions about the contents of the course.
Evaluated Competences:
CB6
CB9
CB10
CT2
CE1
CE3
CE5
CE6
CE9
Problem and/or exercise solving (40):
The students will have to resolve of individual form and in small groups a group of cases and concrete practical exercises.
Evaluated Competences:
CB6
CB9
CB10
CT2
CE1
CE3
CE5
CE6
CE9
Studying time and personal work
Recommended study time for students is about 2 hours per week. Additionally, we estimate that they should spend about 6,5 hours / week working in a number of assignments. All of these activities add up to around 120h/semester.
Subject study recommendations
Subjects that are recommended to be taken simultaneously
Instrumentation and processing for machine vision/V05M185V01104
Real time machine visión/V05M185V01207
Subjects that it is recommended to have taken before
Image description and modeling/V05M185V01102
Fundamentals of image analysis and processing/V05M185V01101