Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Hauptseminar/Master Seminar:
Recent Trends in 3D Computer Vision and Deep Learning

Prof. Nassir Navab, Federico Tombari, Iro Laina, Christian Rupprecht, Helisa Dhamo

Course number: Module IN4826
Type: Advanced Seminar Course Module IN8901
Type: Master Seminar Module IN2107
Type: Seminar Module IN0014
SWS: 2+0
ECTS: 5 Credits
Location: TBD
Time: TBD
Course Language: English


  • The preliminary meeting was on July 6, 16:00, Location: 02.08.011


  • The Seminar Course concerns recent advances in the field of 3D Computer Vision and Deep Learning. The Seminar will propose a list of recent scientific articles related to the main current research topics in the field, such as 3D keypoint detection and description, point cloud matching and registration, 3D object recognition and classification, SLAM, 3D reconstruction, 3D object pose estimation and tracking, depth prediction, 3D scene understanding.



In this Master Seminar (formerly Hauptseminar), each student is asked to select one paper from a list. In order to successfully complete the seminar, participants have to fulfill these requirements:

  • Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
  • Written Report: A document of maximum 8 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
  • Attendance: Participants have to participate actively in all seminar sessions.

The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The report must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both report and presentation have to be done in English.

List of Topics and Material

Preliminary list of papers. Subject to changes.

Date Authors Title Conference Link Tutor Student
Mi 18.10.2017 J. Martinez On human motion prediction using recurrent neural networks CVPR 2017 link Huseyin Kankamon Rujiranun
Mi 18.10.2017 Mi 29.11.2017 A. Alahi et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces CVPR 2016 link Oliver Thomas Baumeister
Mi 08.11.2017 Wang et al. SURGE: Surface Regularized Geometry Estimation from a Single Image NIPS 2016 link Iro Michael Lohr
Mi 08.11.2017 Zhou et al. Unsupervised Learning of Depth and Ego-Motion from Video CVPR 2017 link Iro Thomas Schmid
Mi 22.11.2017 R. Mur-Artal and J. D. Tardos Visual-Inertial Monocular SLAM with Map Reuse RAL 2017 link Chen Xiao Robin Ostner
Mi 22.11.2017 K. Tateno et al. CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction CVPR 2017 link Yida Florian Windolf
Mi 29.11.2017 T. Cavallari et al. On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation CVPR 2017 link Helisa Christoph Gnoth
Mi 29.11.2017 J. Engel et al. Direct sparse odometry PAMI 2017 link Jakob Vera Hug
Mi 06.12.2017 G. Riegler et al OctNetFusion: Learning Depth Fusion from Data 3DV 2017 link Keisuke Rupal Jain
Mi 06.12.2017 Mi 10.01.2018 M. Slavcheva et at. KillingFusion: Non-rigid 3D Reconstruction without Correspondences CVPR 2017 link Patrick Mohammad Ahantab
Mi 13.12.2017 C.R. Qi et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space NIPS 2017 link Johanna Johann Frei
Mi 13.12.2017 J. Wu et al. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling NIPS 2016 link Niko Jonas Gerstner
Mi 10.01.2017 N. Verma et al. Dynamic Filters in Graph Convolutional Networks arxiv 2017 link Tolga Anna Reithmeir
Mi 10.01.2017 F. Monti et al. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs CVPR 2017 link Benni Tobias Valinski
Mi 24.01.2018 M. Rahd and V. Lepetit BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth arxiv 2017 link Fabian Saadhana Boppineni Venkataraman
Mi 24.01.2018 Haoqiang Fan et. al. A Point Set Generation Network for 3D Object Reconstruction from a Single Image arXiv 2017 link Christian Alexei Vasilev


The schedule for the paper presentations will be announced here during the semester. Each student is required to attend all presentations.

Date Time Place Speaker Topic
Mi 18.10.2017 16:00 - 17:30 03.13.010 Kankamon Rujiranun
Human motion prediction
Mi 08.11.2017 16:00 - 17:30 03.13.010 Michael Lohr
Thomas Schmid
Depth estimation
Mi 15.11.2017 16:00 - 17:30 03.13.010   cancelled
Mi 22.11.2017 16:00 - 17:30 03.13.010 Robin Ostner
Florian Windolf
Simultaneous localization and mapping
Mi 29.11.2017 16:00 - 17:30 03.13.010 Christoph Gnoth
Vera Hug
Thomas Baumeister
Mi 06.12.2017 16:00 - 17:30 03.13.010 Rupal Jain
Mi 13.12.2017 16:00 - 17:30 03.13.010 Johann Frei
Jonas Gerstner
Learning geometry
Mi 10.01.2018 16:00 - 17:30 03.13.010 Anna Reithmeir
Tobias Valinski
Mohammad Ahantab
Graph convolutional networks
Mi 24.01.2018 16:00 - 17:30 03.13.010 Saadhana Boppineni Venkataraman
Alexei Vasilev
Learning with objects

Title: Recent Trends in 3D Computer Vision and Deep Learning
Professor: Prof. Nassir Navab, Dr. Federico Tombari
Tutors: Iro Laina, Christian Rupprecht, Helisa Dhamo
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2017WiSe

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