Torsten Sattler: Katalogdaten im Frühjahrssemester 2018 |
Name | Herr Dr. Torsten Sattler |
URL | http://people.inf.ethz.ch/sattlert/ |
Departement | Informatik |
Beziehung | Dozent |
Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|
252-0579-00L | 3D Vision | 4 KP | 3G | T. Sattler, M. R. Oswald | |
Kurzbeschreibung | The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization. | ||||
Lernziel | After attending this course, students will: 1. understand the core concepts for recovering 3D shape of objects and scenes from images and video. 2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications. 3. have a good overview over the current state-of-the art in 3D vision. 4. be able to critically analyze and asses current research in this area. | ||||
Inhalt | The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications. | ||||
263-5904-00L | Deep Learning for Computer Vision: Seminal Work Number of participants limited to 24. | 2 KP | 2S | T. Sattler, L. M. Koch | |
Kurzbeschreibung | This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and discuss the papers and gain an understanding of the most influential research in this area - both past and present. | ||||
Lernziel | The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key contributions to the field of deep learning for vision (including a historical perspective as well as recent work). Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as well as how to give a scientific presentation and lead a discussion on their topic. | ||||
Inhalt | The seminar will start with introductory lectures to provide (1) a compact overview of challenges and relevant machine learning and deep learning research, and (2) a tutorial on critical analysis and presentation of research papers. Each student then chooses one paper from the provided collection to present during the remainder of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants. | ||||
Skript | The selection of research papers will be presented at the beginning of the semester. | ||||
Literatur | The course "Machine Learning" is recommended. |