SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification
The implementation of our paper accepted in ECCV (EUROPEAN CONFERENCE ON COMPUTER VISION, 16th, 2020)
Authors: Yida Wang, David Tan, Nassir Navab and Federico Tombari If you find this work useful in yourr research, please cite:
@article{DBLP:journals/corr/abs-2008-07358,
author = {Yida Wang and
David Joseph Tan and
Nassir Navab and
Federico Tombari},
title = {SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification},
journal = {CoRR},
volume = {abs/2008.07358},
year = {2020},
url = {https://arxiv.org/abs/2008.07358},
archivePrefix = {arXiv},
eprint = {2008.07358},
timestamp = {Fri, 21 Aug 2020 15:05:50 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2008-07358.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}Thanks for @slothfulxtx who has corrected my PyTorch implementation of SoftPool operator!
SoftPoolNet
Object Completion
Soft-Pool Operation
Train
SoftPool operators are provided in Pytorch frameworks, both using CUDA 10.2, we recommend to use the Pytorch version 1.2.0.
As we have some comparison experiments on GRNet and MSN, we suggest that you compile python libs in chamfer_pkg, emd, expansion_penalty and extensions.
You can go towards each folder which includes the mentioend libs by cd, then
python setup.py install --userSuppose that GPU 0 is supposed to get used for training
CUDA_VISIBLE_DEVICES=0 python3 train.py --batch 16 --n_regions 8 --num_points 2048 --dataset shapenet --savepath ijcv_shapenet_softpool --methods softpoolIn case there are pretrained models
CUDA_VISIBLE_DEVICES=0 python3 train.py --batch 16 --n_regions 8 --num_points 2048 --dataset shapenet --savepath ijcv_shapenet_softpool --model log/ijcv_shapenet_softpool/network.pth --methods softpoolBenchmarks
Currently you can train and validate related works which are posted in Complete3D benchmark using the same infrastructure
CUDA_VISIBLE_DEVICES=0 python3 val.py --n_regions 1 --num_points 2048 --model log/ijcv_shapenet_pcn/network.pth --dataset shapenet --methods pcn # PCN
CUDA_VISIBLE_DEVICES=1 python3 val.py --n_regions 1 --num_points 2048 --model log/ijcv_shapenet_pointcnn/network.pth --dataset shapenet --methods pointcnn # PointCNN
CUDA_VISIBLE_DEVICES=0 python3 val.py --n_regions 1 --num_points 2048 --model log/ijcv_shapenet_folding/network.pth --dataset shapenet --methods folding # FoldingNet
CUDA_VISIBLE_DEVICES=0 python3 val.py --n_regions 1 --num_points 2048 --model log/ijcv_shapenet_grnet/network.pth --dataset shapenet --methods grnet # GRNet
CUDA_VISIBLE_DEVICES=0 python3 val.py --n_regions 8 --num_points 2048 --model log/ijcv_shapenet_softpool/network.pth --dataset shapenet --methods softpool # SoftPoolNet
CUDA_VISIBLE_DEVICES=0 python3 val.py --n_regions 1 --num_points 2048 --model log/ijcv_shapenet_msn/network.pth --dataset shapenet --methods msn # MSN
CUDA_VISIBLE_DEVICES=0 python3 val.py --n_regions 1 --num_points 2048 --model log/ijcv_shapenet_pointgcn/network.pth --dataset shapenet --methods pointgcn Listed approaches (until ECCV 2020) are reported in complete3d dataset where you can reproduce our results with scripts in 'benchmark' folder.
