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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

31.08.2022

Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.

More



Practical Course: Geometric Scene Understanding (10 ECTS)

Overview

This practical course aims at advanced students with prior knowledge of deep learning (e.g. Introduction to Deep Learning, IN2346) and multi-view geometry (e.g. Computer Vision II, IN2228). The goal of this course is to gain practical experience with state-of-the-art computer vision models and implement innovative ideas tackling open real-world challenges.

Organisers
News
  • 19.01.2024: The capacity of the course is limited (max. 18 students). To ensure that the most eligible students get a spot, please send us your CV and transcripts to gsu-ss24@vision.in.tum.de by February 14 (single PDF, max. 5MB).
Dates
February 7, 11:00 Preliminary meeting (Slides: Slides)
February 9-14 Pre-course matching (Matching System)
April 17, 11:00–12:00 Presentation of topics (in-person, TBA)
April 18-23 Group-topic matching
May 29, 11:00–13:00 Midterm presentations (in-person, TBA)
July 17, 11:00–13:00 Project presentations (in-person, TBA)
September 30 Project reports due
Course logistics

Course supervisors will initially offer carefully chosen project ideas and scenarios to address. These scenarios are centred around obtaining semantic and/or geometric knowledge (e.g. estimating camera/object pose, video/panoptic segmentation) from visual data (e.g. images, videos, point clouds) using deep learning networks. The problem settings can range from fully supervised to unsupervised formulations. Each project will be assigned to a group of up to three students and supported by an experienced advisor. At the end of the course, the student groups are required to present their project results in class and submit a written report.

Prerequisites: IN2346, IN2228.

Contact

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

31.08.2022

Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.

More