Thanks so much for your works! I try to reproduce the results of PointCNN in Pytorch but still cannot get a reasonable result and I find your project just now. But I have some question about the code you provided.
The network in this repository is some kind of different from the origin one.
1, the author uses three depthwise conv layer to produce the transform matrix X. You just use one depthwise layer plus another two dense layer. It will need more parameters compared to the original and maybe arise overfitting.
2, why do you remove batchnorm in your dense layer? I think this is unreasonable.
3, the configuration is quite different from the original one
in the original code, the output channel has a coefficient 3. And it seems that you use 5 xconv layers while the author uses 4.
4, what's the best accuracy you get on ModelNet40?
Nevertheless, thanks for your sharing. Hope my questions are not too much.
Thanks so much for your works! I try to reproduce the results of PointCNN in Pytorch but still cannot get a reasonable result and I find your project just now. But I have some question about the code you provided.
The network in this repository is some kind of different from the origin one.
1, the author uses three depthwise conv layer to produce the transform matrix X. You just use one depthwise layer plus another two dense layer. It will need more parameters compared to the original and maybe arise overfitting.
2, why do you remove batchnorm in your dense layer? I think this is unreasonable.
3, the configuration is quite different from the original one
in the original code, the output channel has a coefficient 3. And it seems that you use 5 xconv layers while the author uses 4.
4, what's the best accuracy you get on ModelNet40?
Nevertheless, thanks for your sharing. Hope my questions are not too much.