Section: 05 06 图像形成 | 高级计算机视觉

Section outline

Select activity 阅读教材1 第2章图像形成教材2 4.1 DLT算法

阅读教材1 第2章图像形成教材2 4.1 DLT算法 Page

Students must

Mark as done
Select activity Lecture 2.1 (Image Formation： Primitives and Transformations)

Lecture 2.1 (Image Formation： Primitives and Transformations) File

You have to add at least one completion condition.
Select activity Lecture 2.2 (Image Formation： Geometric Image Formation)

Lecture 2.2 (Image Formation： Geometric Image Formation) File

Students must

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Select activity Lecture 2.3 (Image Formation： Photometric Image Formation)

Lecture 2.3 (Image Formation： Photometric Image Formation) File

Students must

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Select activity Lecture 2.4 (Image Formation： Image Sensing Pipeline)

Lecture 2.4 (Image Formation： Image Sensing Pipeline) File

Students must

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Select activity 作业1 image_formation 含计算题和编程题(10.11上课前)

作业1 image_formation 含计算题和编程题(10.11上课前) Assignment

Students must

Mark as done

https://classroom.github.com/a/p_pWDT3Z
git clone https://github.com/GDUTCV/ <作业页面自动生成的> .git
cd hw01_image_formation/code/
Create a new environment named lecturecv and install required packages (numpy, etc.) via running:
conda env create -f environment.yml
Note: A typical source of error is to use an old version of conda itself. You can update it via:
conda update -n base conda -c anaconda
Before launching your notebook you need to activate the environment:
conda activate lecturecv
Depending on your configuration, you might instead need to run:
source activate lecturecv
You can now start jupyter notebook from the directory:
jupyter-notebook
A browser window should be opened in which you can open the notebook of the first exercise called image_formation.ipynb
也可以上传到colab然后做编程题。
也可以上传到google drive再用colab打开，此时需要加载drive文件夹
from google.colab import drive
drive.mount('/content/drive')

本周论文研讨 Page

HAHA: Highly Articulated Gaussian Human Avatars with Textured Mesh Prior	https://arxiv.org/pdf/2404.01053	https://github.com/david-svitov/HAHA/
Emergent Correspondence from Image Diffusion	https://proceedings.neurips.cc/paper_files/paper/2023/file/0503f5dce343a1d06d16ba103dd52db1-Paper-Conference.pdf	https://diffusionfeatures. github.io
Recurrent Partial Kernel Network for Efficient Optical Flow Estimation	https://hmorimitsu.com/publication/2024-aaai-rpknet/2024-aaai-rpknet.pdf	https://github.com/hmorimitsu/ptlflow