Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extracts local features using residual point MLP blocks. In each stage, we first transform the local point using a geometric affine module, and then local points are extracted before and after aggregation, respectively. By repeating multiple stages, PointMLP progressively enlarges the receptive field and models entire point cloud geometric information.
**Questions on ModelNet40 classification results (a common issue for ModelNet40 dataset in the community)**
The performance on ModelNet40 of almost all methods are not stable, see (https://github.com/CVMI-Lab/PAConv/issues/9#issuecomment-873371422).<br>
If you run the same codes for several times, you will get different results (even with fixed seed).<br>
The best way to reproduce the results is to test with a pretrained model for ModelNet40. <br>
Also, the randomness of ModelNet40 is our motivation to experiment on ScanObjectNN, and to report the mean/std results of several runs.
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The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).
On ModelNet40, fixed pointMLP achieves a result of **91.5% mAcc** and **94.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/modelnet40/pointMLP-20220209053148-404/).
On ScanObjectNN, fixed pointMLP achieves a result of **84.4% mAcc** and **86.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/pointMLP-20220204021453/). Fixed pointMLP-elite achieves a result of **81.7% mAcc** and **84.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/model313Elite-20220220015842-2956/).
Stay tuned. More elite versions and voting results will be uploaded.
:point_right::point_right::point_right:**NOTE:** The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).
By default, it will create a folder named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.
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.