122 lines
4.4 KiB
Markdown
122 lines
4.4 KiB
Markdown
# pointMLP-pytorch
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__Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework__
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[](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=rethinking-network-design-and-local-geometry)
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[](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=rethinking-network-design-and-local-geometry)
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[archived: Feb/3/2022]
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<div align="center">
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<img src="overview.png" width="650px" height="300px">
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</div>
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Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extract local features using residual point MLP blocks. In each stage, we first transform local point using a geometric affine module, then local points are are extracted before and after aggregation respectively. By repeating multiple stages, PointMLP progressively enlarge the receptive field and model entire point cloud geometric information.
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## TO DO:
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- [ ] release paper/codes by Feb/7/2022
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- [ ] update std bug (unstable testing)
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- [ ] project page
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## Updates Jan/31/2022:
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We will release an official code here: [PointMLP-pytorch](https://github.com/13952522076/pointMLP-pytorch)
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This anonymous link will expire on: **4/2/2022**
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**Note:** this anonymous link is synchronized with [PointMLP-pytorch@d2b8dba
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](https://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).
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## For rebuttal
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The link to CurveNet on ScanObjectNN : [[link]](https://drive.google.com/drive/folders/1u02_2aK6hfT3Ds81vtd4wv3n3tFGQ3bX?usp=sharing)
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The link to Residual PointNet++ on MOdelNet40: [[link]](https://drive.google.com/drive/folders/1klIpv2QLTVhDWusfQCAMXq-DkYawr-yA?usp=sharing)
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The link to intergrating our Affine to other models: [[DGCNN]](https://drive.google.com/drive/folders/1qDkCKVtF-QXrDceBKAvcoZ4mv9vTaYnR?usp=sharing) [[PointNet++]](https://drive.google.com/drive/folders/1jPfB_8xJjkCQfdRAsL1u6FfABpfFKEC9?usp=sharing)
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To link to more pre-MLP blocks withou pos-MLP blocks: [[link]](https://drive.google.com/drive/folders/1KORIIUZmEJ3FHKPeKj-p8u9m7o5DKnmQ?usp=sharing)
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## Pre-trained models
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Please download the pre-trained models and log files here: [[anonymous google drive]](https://drive.google.com/drive/folders/1Jn9HNpPsrq-1XqSmOUtw4cwPMjsIiIpz?usp=sharing)
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## Install
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Please ensure that python3.7+ is installed. We suggest user use conda to create a new environment.
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Install dependencies
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```bash
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pip install -r requirements.txt
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```
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Install CUDA kernels
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```bash
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pip install pointnet2_ops_lib/.
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```
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## Classification ModelNet40
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The dataset will be automatically downloaded, run following command to train
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```bash
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# train pointMLP
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python main.py --model pointMLP
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# train pointMLP-elite
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python main.py --model pointMLPElite
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# please add other paramemters as you wish.
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```
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By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.
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To conduct voting experiments, run
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```bash
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# please modify the msg accrodingly
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python voting.py --model pointMLP --msg demo
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```
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## Classification ScanObjectNN
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- Make data folder and download the dataset
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```bash
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cd pointMLP-pytorch/classification_ScanObjectNN
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mkdir data
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cd data
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wget http://103.24.77.34/scanobjectnn/h5_files.zip
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unzip h5_files.zip
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```
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- Train pointMLP/pointMLPElite
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```bash
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# train pointMLP
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python main.py --model pointMLP
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# train pointMLP-elite
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python main.py --model pointMLPElite
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# please add other paramemters as you wish.
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```
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By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.
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## Part segmentation
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- Make data folder and download the dataset
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```bash
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cd pointMLP-pytorch/part_segmentation
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mkdir data
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cd data
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wget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate
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unzip shapenetcore_partanno_segmentation_benchmark_v0_normal.zip
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```
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- Train pointMLP
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```bash
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# train pointMLP
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python main.py --model pointMLP
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# please add other paramemters as you wish.
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```
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