Computer Vision

Dr. Bogdan Savchynskyy, WiSe 2024/25

This seminar belongs to the Master in Physics (specialization Computational Physics, code "MVSem") and Master of Data and Computer Science, Applied Informatics (code "IS") , but is also open for students of Scientific Computing and anyone interested.

Summary

The topic of this semester is

Video-Based Scene Analysis.

We will consider inference and learning techniques for these problems as well as the related applications in computer vision.

General Information

Please register for the seminar in Müsli. The first seminar will take place on Thursday, October 17 at 14:00. Please make sure to participate!

  • Seminar: Thu, 14:00 – 16:00 in Mathematikon B (Berliner Str. 43), SR B128
    Entrance through the door at the side of Berlinerstrasse. Ring the door bell labelled "HCI am IWR" to be let in. The seminar room is on the 3rd floor.
  • Credits: 4/ 6 CP depending on course of study.

Seminar Repository:

Slides and schedule of the seminar will be placed in HeiBox .

Papers to Choose from:

[1] First Single View Depth: Eigen, David and Puhrsch, Christian and Fergus, Rob: „Depth map prediction from a single image using a multi-scale deep network“

[2] Depth Anything: Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao: „Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data“ (Depth Anything V2 Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao )

[3] MIDAS: Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler and Vladlen Koltun: „Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer“

[4] Marigold Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler: „Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation“

[5] Multi & Single View Fusion: JunDa Cheng, Wei Yin, Kaixuan Wang, Xiaozhi Chen, Shijie Wang, Xin Yang: „Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving“

[6] ZeroDepth V. Guizilini, Igor Vasiljevic, Di Chen, Rares Ambrus, Adrien Gaidon: „Towards Zero-Shot Scale-Aware Monocular Depth Estimation“

[7] HybridDepth: Ashkan Ganj, Hang Su, Tian Guo: „ HybridDepth: Robust Depth Fusion for Mobile AR by Leveraging Depth from Focus and Single-Image Priors“

[8]Zhong Liu, Ran Li, Shuwei Shao, Xingming Wu, Weihai Chen: „Self-Supervised Monocular Depth Estimation With Self-Reference Distillation and Disparity Offset Refinement“

[9] MAMo R. Yasarla, H. Cai, Jisoo Jeong, Y. Shi, Risheek Garrepalli, F. Porikli: „MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation“

[10] MDSNet Jiaqi Zhao, Chaoyue Zhao, Chunling Liu, Chaojian Zhang, Wang Zhang: „MDSNet: self-supervised monocular depth estimation for video sequences using self-attention and threshold mask“

[11] Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, I. Reid: „Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video“

[12] M4Depth: Michaël Fonder, D. Ernst, Marc Van Droogenbroeck: „M4Depth: A motion-based approach for monocular depth estimation on video sequences“

[13] METER: Lorenzo Papa, Paolo Russo, Irene Amerini: „ METER: A Mobile Vision Transformer Architecture for Monocular Depth Estimation“

[14] CORNet Xuyang Meng, Chunxiao Fan, Yue Ming, Hui Yu : „CORNet: Context-Based Ordinal Regression Network for Monocular Depth Estimation“

[15] LapDepth Minsoo Song, Seokjae Lim, Wonjun Kim: „ Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals“

[16] Rui Wang, S. Pizer, Jan-Michael Frahm: „Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth“

[17] CoMoDA Yevhen Kuznietsov, M. Proesmans, L. Gool: „CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences“

[18] OD-MVSNet Ke Pan, Kefeng Li, Guangyuan Zhang, Zhenfang Zhu, Peng Wang, Zhenfei Wang, Chen Fu, Guangchen Li, Yuxuan Ding:„OD-MVSNet: Omni-dimensional dynamic multi-view stereo network“

[19] Depth&Pose Estimation Dinh-Cuong Hoang, Phan Xuan Tan, Thu-Uyen Nguyen, Hai-Nam Pham, Chi-Minh Nguyen, Son-Anh Bui, Quang-Tri Duong, van-Duc Vu, van-Thiep Nguyen, van-Hiep Duong, Ngoc-Anh Hoang, Khanh-Toan Phan, Duc-Thanh Tran, Ngoc-Trung Ho, Cong-Trinh Tran:„A Unified Framework for Depth-Assisted Monocular Object Pose Estimation“

[20] BaseBoostDepth: Kieran Saunders, Luis J. Manso, George Vogiatzis:“BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth Estimation“

[21] Canbin Li, Yiguang Liu: „Multi-Scale Depth Guidance Transformer for Monocular Depth Estimation“

[22] Dinh-Cuong Hoang, Phan Xuan Tan, Thu-Uyen Nguyen, Hai-Nam Pham, Chi-Minh Nguyen, Son-Anh Bui, Quang-Tri Duong, van-Duc Vu, van-Thiep Nguyen, van-Hiep Duong, Ngoc-Anh Hoang, Khanh-Toan Phan, Duc-Thanh Tran, Ngoc-Trung Ho, Cong-Trinh Tran: „A Unified Framework for Depth-Assisted Monocular Object Pose Estimation“

Presentation schedule

TBA

Contact

Dr. Bogdan Savchynskyy
In case you contact me via email, its subject should contain the tag [SemCV]. Emails without this tag have a very high chance to be lost and get ignored therefore!