章节大纲
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学生必须标记完成
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')
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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
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