Teaching

Summer 2023

Photorealistic 3D Reconstruction with Deep Learning

Computer vision has led to many recent technology break-throughs and is one of the most demanded fields. Even more, 3D computer vision is becoming increasingly important and the field has recently shown remarkable progress.

In this seminar, we will look at one of the most important aspects of understanding the 3D world: joint reconstruction of geometry and materials. While recent approaches based on NeRF, NeuS and DeepSDF have shown remarkable progess in reconstructing the geometry, material reconstruction is today still largely neglected. We expect that reconstructing materials will be key to the next generation of 3D computer vision algorithms.

The seminar will bring you up to speed with the concepts and state-of-the-art literature. After few introductory lectures, the seminar will continue with presentations to review the most important and most recent papers in the field. Overall, the seminar will set you up to be familiar with distinguished literature and enable you to start research in the field.

Every student is expected to give a 30min presentation, followed by a 15min discussion and hand in a write-up at the end of the seminar. Apart from the technical content, we offer mentoring on how to hold compelling presentations. This provides you the opportunity to learn key skills for job applications and your later career.

The seminar is offered by the Computer Vision and Perception Lab (https://cvmp.cs.uni-saarland.de/) that focuses on building the next generation machine perception algorithms. The lab is offering Master’s thesis, Hiwis and PhD positions. Please contact ilg<at>cs.uni-saarland.de if you are interested.

Requirements: A background in deep learning and computer vision is required. A background in computer graphics is helpful.

Places: 12

Winter 2022/2023

3D Object Representation and Reconstruction with Machine Learning

Computer vision has led to many recent technology break-throughs and is currently one of the most demanded fields. Due to the statistical and complex nature of the world, it is also one of the hardest disciplines. Deep learning has proven to be the method of choice and nurtured the successes.

However, most deep-learning approaches for computer vision are constructed in 2D. One can argue that they do not understand the 3D world and have inherent limitations. Therefore, 3D computer vision is already becoming increasingly more important and will likely lead the next generation of algorithms.

The seminar will bring you up to speed with the concepts and state-of-the-art literature of 3D representations and 3D reconstruction with deep learning. After few introductory lectures, the seminar will continue with presentations to review the most important and most recent papers in the field, covering SDF- and occupancy-based representations as well as neural radiance fields (NeRFs). Overall, the seminar will set you up to be familiar with distinguished literature and enable you to start research work in the field.

Every student is expected to give a 45min presentation with a write-up and an implementation.

The seminar is offered by the new Computer Vision and Perception Lab (https://cvmp.cs.uni-saarland.de/) that focuses on building the next generation machine perception algorithms, which are not rigid but able to adapt to their environment and evolve. The lab is currently offering Master’s and PhD positions.

Requirements: This seminar will focus on state-of-the-art research and is for advanced students who are already acquainted with machine learning. In particular, a basic understanding of projection and 3D geometry and prior experience in convolutional neural networks, as well as hands-on implementation of neural networks with pytorch is required. It is recommended to have attended the High Level Computer Vision Lecture. Prior attendance of Computer Graphics may be helpful but is not required.

Places: 20