Hi Menghao,

Thank you for sharing the interesting paper!

I have some queries about the definition of the permutation invariance. In my opinion, though the self-attention calculates the global contextual information and aggregate the feature via weighted summation, the resulting features are still related to the point order (but the feature of the same point seems to be invariant). And I also observe the implementation of max-pooling strategy, which is proposed in PointNet to guarantee invariance.

So I wonder how you define the permutation invariance, because it seems the attention itself can hardly gurantee the global invanriance. Thank you very much!

Rui

Dear MenghaoGuo，

感谢你的代码，我在你的文章中的3.4节看到local feature representation，但是似乎这个操作只有cls里面有，seg文件中并没有文章提到的SG layer。 是分割任务中不需要local feature吗，还是global feature就已经能胜任分割任务？ 希望您解答我的问题。

Thanks again！

Hi, thanks for your great work! However, I have a problem with the concatenation in segmentation. As shown in Fig. 2, the global feature is obtained by repeating its previous vector. It should be (batch_size, 1024, point_num), point_num may be 1024 in the modelnet. While the point feature should be (batch_sie, 1024, sampled_point_num). For example, the sample_point_num is 256 according to the implementation at https://github.com/Strawberry-Eat-Mango/PCT_Pytorch. So how can these two features be concatenated? Or do we just segment those sampled points?

Hi, @MenghaoGuo ,

Thanks for releasing the package. The current package only provides the code for network construction. Could you provide a real example code for point cloud classification or segmentation?

Thanks~

Hello,

very interesting paper, and nice to publish parts of the code along with it!

A couple of questions:

• I was wondering why the layer calculating the values self.v_conv has a bias attached to it. Looking at other 'attention' implementations, it seems that those mostly exclude bias from it (as you also do for the keys and queries). Did you see any improvement adding a bias there?
• Is there any reason for setting the initial weights of the key and query layer equally?
• In the paper, you mention making use of Farthest Point Sampling (FPS) for the neighbor embedding module, but before you sample you embed the pure 3-dimensional point-coordinates in a high-dimensional space. Do you perform FPS in the full 64-dimensional space, or do you do this in the 3-dimensional one?

Kind regards, steven

Thank you very much for your work. I would like to ask how to generate the feature map of the point cloud. If it is convenient, can you provide an example? thanks!

Hi, @MenghaoGuo,

For Figure 1 in your paper, which self-attention layer is used to visualize the attention map? From your implementation, there are 4 self-attention layers (SA1, SA2, SA3, SA4) in the model.

Thanks~

I am trying to run simple optimization using this segmentation netowrk, but I cannot fully understand what cls_label parameter in partseg network.

Do I have to run classification before conducting segmentation or something like that?

Thank you.

HI, @MenghaoGuo ,

From the code in cls and partseg, the attention weights are already normalized by self.softmax(). Why did you add an extra line attention / (1e-9 + attention.sum(dim=1, keepdims=True)) for weight normalization ?

Any particular reason?

Thanks~

Hi,

amazing work and great results, thanks for making it available here! I was wondering whether you plan to release the pre-trained models? I work on robotic grasping and it would be interesting to see how your architecture performs for this task compared to other state-of-the-art models.

Hello,

I really appreciate to your creative work.

However, I hope to know why did you use same weight value when initialize the q_conv and k_conv kernel?

As I know, they don't have to be identical.

Is there any reason?

Thank you for your work again.

I'm very sorry to bother you. I trained the PCT in a 2080Ti，but I can‘t obtain the accuracy to 93.2 and the result is greatly waving. I only obtain the accuracy to 92.8. Do you have other particular parameter? Best regard，

Hi,

PCT used the Batch normalization, instead of the Layer normalization, used by original Transformer.

I wonder how do you consider about Layer normalization and Batch normalization in PCT?

Hello, thanks for your work! I am reading the code but get confused about some details. In the class Point_Transformer_Last, the self.pos_xyz is not defined in the section. What does the operation specifically do and where can I find the definition of it? Appreciate!

When the convolution weight of q and k are initialized equal, self.q_conv.conv.weight = self.k_conv.conv.weight will it cause q and k to be always the same when updated?