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

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

PD Federico Tombari, Fabian Manhardt, Johanna Wald

Course number: Module IN4826
Type: Advanced Seminar Course Module IN8901, Master Seminar Module IN2107, Seminar Module IN0014
SWS: 2+0
ECTS: 5 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Wednesday, 10.00am-noon
Course Language: English



  • 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, 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

Authors Title Conference Link Tutor Student  
Wang et al. Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation CVPR 2019 paper Fabian Manhardt Vladimir  
Kanazawa et al. Learning 3D Human Dynamics from Video CVPR 2019 paper Manuel Nickel Lukas  
Peng et al. SFV: Reinforcement Learning of Physical Skills from Videos SIGGRAPH ASIA 2018 paper Manuel Nickel Chantal Marie  
Schops et al. BAD SLAM: Bundle Adjusted Direct RGB-D SLAM CVPR 2019 paper Yanyan Lil Lex  
Geneva et al. An Efficient Schmidt-EKF for 3D visual-inertial SLAM CVPR 2019 paper Shun-Cheng Wu Zechen  
Kato et al. Neural 3D Mesh Renderer CVPR 2018 paper Mahdi Saleh Anna  
Kanazawa et al. Learning Category-Specific Mesh Reconstruction from Image Collections ECCV 2018 paper Fabian Manhardt Mert  
Yao et al. 3D-Aware Scene Manipulation via Inverse Graphics NeurIPS? 2018 paper Helisa Dhamo Sina  
Graham et al. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks CVPR 2018 paper Johanna Wald Aleksandr  
Liu et al. FlowNet3D?: Learning Scene Flow in 3D Point Clouds CVPR 2019 paper Mahdi Saleh Stefan  
Shi et al. PointRCNN?: 3D Object Proposal Generation and Detection from Point Cloud CVPR 2019 paper Johanna Wald Madhan  
Park et al. DeepSDF?: Learning Continuous Signed Distance Functions for Shape Representation CVPR 2019 paper Nikolas Brasch Can  
Xu et al. DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction CVPR 2019 paper Nikolas Brasch Philipp  
Li at al. RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion CVPR 2019 paper Yida Wang Alexander  


Each student is required to attend all presentations.

Date Time Place Speaker Topic
Fri, October 18, 2019 16:00-18:00 3.13.010, Seminarraum Federico Tombari Introductory talk
Wed, November 6, 2019 10:00-12:00 3.13.010, Seminarraum   Pose Estimation (6D Object / Human)
Wed, November 27, 2019 10:00-12:00 3.13.010, Seminarraum   Neural 3D Rendering
Wed, December 4, 2019 10:00-12:00 3.13.010, Seminarraum   SLAM
Wed, December 11, 2019 10:00-12:00 3.13.010, Seminarraum   3D Scene Understanding
Wed, December 18, 2019 10:00-12:00 3.13.010, Seminarraum   Object & Scene Reconstruction / Completion
Wed, January 8, 2020 10:00-12:00 3.13.010, Seminarraum   Backup
Wed, January 15, 2020 10:00-12:00 3.13.010, Seminarraum   Backup

Title: Recent Trends in 3D Computer Vision and Deep Learning
Professor: PD Dr. Federico Tombari
Tutors: Fabian Manhardt; Johanna Wald
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2019WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 20 Sep 2019 - 08:15 - ShunChengWu

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