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zhangych02 b357a6b22a | 1 year ago | |
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PU-GCN-src | 2 years ago | |
PU-GCN-tl | 2 years ago | |
LICENSE | 1 year ago | |
PUGCN.pdf | 2 years ago | |
README.md | 1 year ago | |
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PU-GCN is a point cloud upsampling algorithm based on deep learning, which is collected into our open source algorithm library of point cloud. The original project is run on python-3 and tensorflow-1.13, which is in folder './PU-GCN-src'. We expande the Tensorlayer version which is in folder './PU-GCN-tl'.
This algorithm: 1) propose a GCN based point cloud upsampling module called NodeShuffle, which improve state-of-the-art upsampling pipelines when it is used in place of the original upsampling; 2) introduce the Inception DenseGCN to encode multi-scale information; 3) introduce a new large-scale dataset PU1K for point cloud upsampling.
wget https://repo.anaconda.com/archive/Anaconda3-2019.07-Linux-x86_64.sh
bash Anaconda3-2019.07-Linux-x86_64.sh
conda remove --name pugcn --all
conda create -n pugcn python=3.6.8 cudatoolkit=10.1 cudnn numpy=1.16
conda activate pugcn
pip install matplotlib tensorflow-gpu==2.3.1 open3d==0.9 sklearn Pillow gdown plyfile
cd tf_ops
bash compile.sh linux
python main.py --phase train --model pugcn --upsampler nodeshuffle --k 20
cd evaluation_code
bash compile.sh
bash test_pu1k_allmodels.sh
Pretrained Models:/pretrain/pu1k-pugcn
Upsampled Points:/evaluation_code/result
Metrics: /pretrain/pu1k-pugcn/evaluation.csv
We compare the algorithm performance of different frameworks under tensorflow and tensorlayer respectively. The main evaluation metrics are CD, HD and P2F.
Paper results:
CD | HD | P2F |
---|---|---|
0.585 | 7.577 | 2.499 |
Tensorflow:
CD | HD | P2F |
---|---|---|
0.630 | 9.768 | 2.613 |
Tensorlayer:
CD | HD | P2F |
---|---|---|
0.646 | 9.251 | 2.635 |
name: Zhang Yongchi
email: zhangych02@pcl.ac.cn
No Description
Unity3D Asset Python C++ C Cuda other
MIT
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