Praktikum on 3D Computer Vision

Overview

This course focuses on different recent topics in 3D computer vision and their application. A first set of (invited) lectures on specific vision problems (such as 3D shapes, point cloud processing, 6D Pose Estimation, Neural Rendering, 3D humans / matching, SLAM) lays the foundation for practical projects carried out by the student. The projects are motivated from real problems and recent research directions from industrial and academic partners. These are addressed by a group of students with support of coaches from both industry and academia. The proposed solutions to the 3D vision topics are finally presented with at an internal workshop. After the course, the student is familiar with selected topics and recent advances in 3D computer vision for which a practical project has been carried out and experience in problem solving in constant exchange with group members and vision experts has been learnt.

Praktikum on 3D Computer Vision (IN2106, IN4313)

Lecturer (assistant)
Number0000000689
TypePractical course
Duration6 SWS
TermWintersemester 2021/22
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline

Dates

Admission information

See TUMonline
Note: For registration you have to be identified in TUMonline as a student.

Objectives

Please apply for this lab course by following the instructions on the course website. We will select appropriate candidates based on their background, interests, and motivation. For application, please use the provided form, include an up to date score sheet and a paragraph explaining your motivation for applying to this course (300 words max). Students from the informatics faculty also have to register through the common IN.TUM-Matching-System.

Description

This course focuses on different recent topics in 3D computer vision and their application. A first set of (invited) lectures on specific vision problems (such as 3D shapes, point cloud processing, 6D Pose Estimation, Neural Rendering, 3D humans / matching, SLAM) lays the foundation for practical projects carried out by the student. The projects are motivated from real problems and recent research directions from industrial and academic partners. These are addressed by a group of students with support of coaches from both industry and academia. The proposed solutions to the 3D vision topics are finally presented with at an internal workshop. After the course, the student is familiar with selected topics and recent advances in 3D computer vision for which a practical project has been carried out and experience in problem solving in constant exchange with group members and vision experts has been learnt.

Prerequisites

Students should have basic knowledge in C++ and Python programming as well as in computer vision concepts. Ideally, experience with OpenCV (or a similar library) exists and methods such as camera calibration and nonlinear optimization are known.

Teaching and learning methods

- Lectures on specific 3D computer vision topics - Individual assignements - Group project and presentation

Examination

Immanent evaluation based on individual assignments, course participation, group project

Links