Recent Trends in 3D Computer Vision & Deep Learning
Overview
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.
Preliminary Meeting
The preliminary meeting will be held on Friday February 4th at 2 pm via Zoom.
Slides are available here.
List of Topics and Material
Authors | Title | Venue | Link | Tutor | Student |
| Hughes et al. | Hydra: A Real-time Spatial Perception Engine for 3D Scene Graph Construction and Optimization | arXiv 2022 | paper | Shun Cheng | Felix |
| Talak et al. | Neural Trees for Learning on Graphs | NeurIPS 2021 | paper | Evin | Alara |
| Sun et al. | NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video | CVPR 2021 | paper | Yan Di | Alexiy |
| Cao et al. | MonoScene: Monocular 3D Semantic Scene Completion | CVPR 2022 | paper | Markus | Daniel |
| Huang et al. | DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors | CVPR 2021 | paper | Shun Cheng | Hanfeng |
| Zheng et al. | Deep Implicit Templates for 3D Shape Representation | CVPR 2021 | paper | Mahdi | Asli |
| Lin et al. | BARF: Bundle-Adjusting Neural Radiance Fields | ICCV 2021 | paper | Stefano | Eser Murat |
| Or-El et al. | StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation | CVPR 2022 | paper | Evin | Saketh |
| Deng et al. | Depth-supervised NeRF: Fewer Views and Faster Training for Free | CVPR 2022 | paper | Hyun Jun | Christian Tim |
| Xu et al. | SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation | ICCV 2021 | paper | Artem | Nils |
| Wang et al. | Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark | ICCV 2021 | paper | Stefano | Hesham |
| Chen et al. | Geosim: Realistic video simulation via geometry-aware composition for self-driving | CVPR 2021 | paper | Alexander | Remi |
Seminar - Recent Trends in 3D Computer Vision & Deep Learning (IN0014, IN2107, IN4826)
| Lecturer (assistant) | |
|---|---|
| Number | 0000004839 |
| Type | Seminar |
| Duration | 2 SWS |
| Term | Sommersemester 2022 |
| Language of instruction | English |
| Position within curricula | See TUMonline |
| Dates | See TUMonline |
Dates
- 04.02.2022 14:00-14:30 Online: Videokonferenz / Zoom etc.
- 06.05.2022 14:00-16:00 03.13.010, Seminarraum, Introductory talk
- 20.05.2022 14:00-16:00 03.13.010, Seminarraum, Topic: Scene understanding Students: Felix, Alara, Alexiy
- 10.06.2022 14:00-16:00 01.09.014, Seminarraum, Topic: Nerf Students: Eser, Saketh, Christian
- 01.07.2022 14:00-16:00 03.13.010, Seminarraum, Topic: 3D reconstruction Students: Daniel, Hanfeng, Asli
- 08.07.2022 14:00-16:00 03.13.010, Seminarraum, Topic: Autonomous driving Students: Nils, Hesham, Remi
- 29.07.2022 14:00-16:00 03.13.010, Seminarraum, Backup: Asli, Hesham
Admission information
See TUMonline
Note: Please register in the matching system for the course registration (http://docmatching.in.tum.de). Interested students should attend the preliminary meeting.
Note: Please register in the matching system for the course registration (http://docmatching.in.tum.de). Interested students should attend the preliminary meeting.
Description
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.